Predicting coronary artery disease and risk of cardiovascular events

ABSTRACT

Methods of assessing the risk of cardiovascular disease in a subject by detecting the level of at least one metabolite in a sample from the subject are disclosed herein. The level of the metabolite is indicative of the risk of cardiovascular disease in the subject. The metabolites may be acylcarnitines, amino acids, ketones, free fatty acids or hydroxybutyrate. The cardiovascular disease may be risk of a cardiovascular event, presence of coronary artery disease or risk of development of coronary artery disease.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 61/159,077 filed Mar. 10, 2009, which is incorporatedherein by reference in its entirety.

BACKGROUND OF THE INVENTION

Coronary artery disease (CAD) is the leading cause of death inindustrialized countries, and in concert with the epidemic of obesityand diabetes, is rapidly becoming the leading cause of death indeveloping countries. The genetic predilection of CAD iswell-established; family history has been shown to be an independentrisk factor for CAD, especially in early onset forms. Despite this, thegenetic architecture of CAD remains largely unknown.

Many accepted risk factors for CAD are metabolic. However, there remainsan incomplete mechanistic understanding of CAD risk, and equallyimportant, a need to refine our ability to identify individuals athighest risk of cardiovascular events. Given the complex nature of CAD,evaluation with more comprehensive tools may improve risk stratificationand enhance our understanding of the disease process.

SUMMARY OF THE INVENTION

In one aspect, methods for assessing risk of cardiovascular disease in asubject are provided. The risk assessment may include predicting thelikelihood a subsequent cardiovascular event such as a myocardialinfarction, predicting development of CAD, or discriminating thepresence of CAD in a subject. The methods include detecting at least onemetabolite in a sample from the subject. The metabolite may be anacylcarnitine, an amino acid, a ketone, a free fatty acid orβ-hydroxybutyrate. The levels of metabolites are then compared to astandard or to control subjects and can be used to determine the levelof risk of a cardiovascular event, the risk of development of CAD or thepresence of CAD in the subject.

In another aspect, methods of developing a treatment plan for a subjectwith or at risk of developing CAD or a subject at risk for acardiovascular event are also provided. The methods include using thelevel of detected metabolite in the subject to develop a treatment planbased on the risk of cardiovascular disease in the subject. The plan mayinclude diet, exercise and pharmaceutical treatment options.

In still another aspect, methods for assessing the risk ofcardiovascular disease in a subject are provided in which a sample isobtained from the subject. The sample is provided to a laboratory fordetection of metabolite levels in the sample. The metabolites detectedmay be acylcarnitines, amino acids, ketones, fatty acids orhydroxybutyrate. The laboratory returns a report indicating metabolitelevels in the sample, which are indicative of the risk of cardiovasculardisease in the subject.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a set of graphs showing the receiver operating characteristic(ROC) curves for metabolite factors and CAD. ROC curves and measures ofmodel fit (c-statistic) are presented for three models: a clinical modelinclusive of traditional CAD risk factors (diabetes, hypertension,dyslipidemia, smoking, BMI, family history; and for the replicationgroup, age, race and sex are also included) (black line); a modelinclusive of all traditional risk factors plus metabolite factors 4 and9 (gray line); and a model inclusive of all traditional risk factorsplus all metabolite factors (dashed black line). The top graph shows theinitial group and the bottom graph shows the replication group.

FIG. 2 is a set of graphs showing the cox proportional hazards model forpredictive capability of metabolite Factor 8 for cardiovascular events.Unadjusted (left panel) and adjusted (right panel) survival curves(adjusted for BMI, severity of CAD, hypertension, dyslipidemia,diabetes, smoking, family history, ejection fraction, serum creatinine,subsequent CABG, age, race and sex) are presented for metabolite factor8.

FIG. 3 is a pedigree of the eight multiplex GENECARD families. Blackfilled in symbols signify affected with premature CAD; smaller graycircles signify blood profiling performed for this study. Note that themajority of the family members profiled are as-of-yet unaffectedoffspring of the original affected-sibling pairs.

FIG. 4 is a graph showing the heritabilities of conventionalmetabolites. The Y-axis is the negative log 10 of the p-value for theheritability estimate (X-axis). Error bars around heritability pointestimates are in light grey.

FIG. 5 is a graph showing the heritabilities of amino acids and freefatty acids. Displayed are heritabilities of amino acids and free fattyacids. The Y-axis is the negative log 10 of the p-value for theheritability estimate (X-axis). Error bars around heritability pointestimates are in light grey.

FIG. 6 is a graph showing the heritabilities of acylcarnitines. TheY-axis is the negative log 10 of the p-value for the heritabilityestimate (X-axis). Error bars around heritability point estimates are inlight grey.

DETAILED DESCRIPTION

Metabolomics, the study of small-molecule metabolites, may be useful fordiagnosis of human disease. Studies have demonstrated heritability ofmetabolites in plants and mice. As described in the Examples, metaboliteprofiles are heritable in human families with early-onset CAD,suggesting that the known heritability of CAD may be mediated at leastin part through metabolic components measurable in blood. The Examplesdescribe quantitative profiling of 69 metabolites, includingacylcarnitine species (byproducts of mitochondrial fatty acid,carbohydrate and amino acid oxidation), amino acids and conventionalmetabolites such as free fatty acids, ketones and β-hydroxybutyrate, inparticipants enrolled in the Duke CATHGEN biorepository and in familiesselected from the Duke GENECARD study. The capability of metaboliteprofiles to assess the risk of cardiovascular disease in a subject isprovided herein. The Examples demonstrate that the levels of particularmetabolites, alone or in combination, discriminate the likelihood ofdeveloping CAD, the presence of CAD and the risk of subsequentcardiovascular events.

Methods of assessing or predicting risk of cardiovascular disease in asubject are provided. The methods include detecting the level of atleast one metabolite in a sample from the subject. The amount orrelative level of the metabolite may be detected. The metabolitesdetected may be acylcarnitines, amino acids, ketones, free fatty acids(FFA), or hydroxybutyrate. The level of the metabolite in the samplefrom the subject is then compared to a standard to assess the risk ofcardiovascular disease. The standard may be an empirically derivednumber for each metabolite indicating a normal range and/or a rangeindicative of cardiovascular disease or may be direct comparison to thelevels of metabolite in individuals with known cardiovascular diseasestatus.

Methods for assessing the risk of cardiovascular disease in a subject byobtaining a sample from the subject and providing the sample to alaboratory for detection of metabolite levels in the sample are alsoprovided. As above, the metabolite detected by the laboratory mayinclude acylcarnitines, amino acids, ketones, fatty acids andhydroxybutyrate. A report indicating metabolite levels in the sample isthen received from the laboratory. The report indicates the level of themetabolite in the subject and the level can be used to compare tostandard values to indicate the risk of cardiovascular disease in thesubject.

The risk of cardiovascular disease includes assessing the risk of asubject without CAD developing CAD over time due to heritable factors,assessing the presence or absence of CAD in a subject and assessing therisk of having a cardiovascular event. Cardiovascular events includemyocardial infarction, stroke and death.

Subjects may be any mammal, suitably the subject is human. Subjectsidentified as having or at risk of developing CAD may be furtherassessed to determine their risk of a cardiovascular event using themethods provided herein. The methods may be used to help diagnose thepresence of CAD in a non-invasive fashion and/or to develop a treatmentplan for subjects identified as at risk for CAD, having CAD or at riskfor a cardiovascular event. The treatment plan may include provision ofdietary, exercise, and pharmaceutical therapies to the subject. Acardiovascular event includes, but is not limited to, myocardialinfarction (MI), stroke and death.

The metabolite may be detected using a variety of samples, several ofwhich will be apparent to those skilled in the art. In the Examples,peripheral blood was obtained from the subject and processed in order todetect the level of metabolites in the subject. Other tissues or fluidsfrom the subject may also be used, including but not limited to blood,plasma, urine, serum, saliva, and tissue biopsies.

Any method may be used to detect the metabolite. Suitably the method isquantitative such that the level or amount of the metabolite in thesubject or a sample from the subject may be determined. In the Examples,the level of the metabolites was detected by mass spectrometry. Othermethods of measurement may be used, including nuclear magnetic resonance(NMR). The metabolites may also be detected using colorimetric orfluorometric assays based on detection of the metabolite by an assaysuch as a binding or enzymatic assay. Any suitable assay method for themetabolites may be used. Such methods will be apparent to those skilledin the art. The level of the metabolite in the subject may be reportedas ng/ml of metabolite in blood or tissue, by the mM or μM concentrationof the metabolite in the blood or tissue or by using arbitrary units toshow relative levels amongst subjects. In the Examples, the mM or μM ofmetabolite in the blood are reported.

In some embodiments, detection of a single metabolite is sufficient toassess risk of cardiovascular disease. In other embodiments, 2, 3, 4, 5,7, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60 or 65 metabolites may bedetected and used in the methods to assess the risk of cardiovasculardisease. The metabolites detected may be related in a factor byprincipal component analysis of a population of subjects. The factors,or groups of metabolites, useful for assessing heritability of CAD andfor the presence of CAD or risk of having a cardiovascular event arepresented in the Examples below.

The level of metabolite in the subject is used to determine whether thesubject has CAD, risk of the subject developing CAD and/or the risk ofthe subject experiencing a cardiovascular event in the future. The levelof risk determination may be based on a standard level of the metabolitepresent in the blood. Such a standard is used for the relationshipbetween HDL and LDL cholesterol measurements in which risk for CAD ispredicted when cholesterol levels reach certain level in the blood afterfasting and the ratio of HDL to LDL is beyond standard limits. Suchstandards are generally developed based on a large population study.Alternatively, the determination of risk may be based on directcomparison to one or more control subjects. For example, a set ofcontrol subjects lacking CAD and with no cardiovascular events in thetwo years following sample procurement and a set of control subjectswith CAD and with or without a cardiovascular event in the two yearsfollowing sample procurement could be used as a comparison.

The risk of cardiovascular disease in the subject may be expressed inrelative terms. For instance a normal level of a metabolite may bereferred to as 1.0 in subjects at low to average risk for cardiovasculardisease such as CAD or a cardiovascular event. Any numbers below 1.0could indicate the subject has a lower risk than the general populationrisk. A number greater than 1.0 would indicate that the subject has agreater than average risk level and the actual number could relate tothe level of risk. For example, a subject whose metabolite level is 2.0may be two times as likely to experience a cardiovascular event in thenext two years as compared to an average individual.

The assessment of risk of cardiovascular disease, including CAD or afuture cardiovascular event, includes but is not limited to, developinga risk profile. The assessment or prediction may indicate that thesubject is 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 200%,300%, 400%, 500%, 750% or 1000% more likely to have or develop acardiovascular disease, such as CAD or have cardiovascular event, than acontrol subject. A control subject is an individual that does not haveCAD and possesses levels of the metabolite that do not correlate with anincreased risk of CAD or a cardiovascular event.

The metabolites predictive of risk of developing cardiovascular diseaseCAD include metabolites involved in many of the major pathways of lipid,protein and carbohydrate metabolism. Thus, the acylcarnitines includeacetyl carnitine (C2), a by-product of glucose, fatty acid and aminoacid metabolism, Propionyl carnitine (C3) and Isoveleryl carnitine (C5),which provide information on amino acid catabolism, the dicarboxylatedacylcarnitine, which report on peroxisomal fatty acid metabolism, andthe medium-long chain acylcarnitines, which are intermediates inlong-chain fatty acid beta-oxidation. The amino acids serve as importantintermediates in protein turnover and catabolism and the ketones are anindex of fatty acid beta-oxidation. Table 1 below shows the short andfull-names of the metabolites tested in the Examples. Table 2 shows thebiological functions, if available, of each of the tested metabolites.

TABLE 1 Nomenclature and Intra-lndividual Variability of Metabolites.Measures of Intra-Individual Variability Short Name Full Metabolite NameR² CV C2 Acetyl carnitine 0.84 5.16 C3 Propionyl carnitine 0.75 22.95C4: Ci4 Butyryl carnitine or Isobutyryl carnitine 0.75 13.48 C5: 1Tiglyl carnitine 0.46 11.24 C5 Isovaleryl carnitine, 3-methylbutyrylcarnitine or 0.51 13.08 2-Methylbutyryl carnitine C4-OHβ-Hydroxy-butyryl carnitine 0.40 15.46 Ci4-DC: C4DC Methylmalonylcarnitine or Succinyl carnitine NA NA C8: 1 Octenoyl carnitine 0.8611.29 C8 Octanoyl carnitine 0.96 4.49 C8: 1-OH/ 3-Hydroxy-cis-5-octenoylcarnitine or Hexenedioyl NA NA C6: 1-DC carnitine C8: 1-DC Octenedioylcarnitine NA NA C5-DC Glutaryl carnitine 0.60 10.33 C6-DC Adipoylcarnitine 0.54 13.33 C10: 3 Decatrienoyl carnitine 0.74 8.78 C10: 1Decenoyl carnitine 0.99 3.57 C10 Decanoyl carnitine 0.90 8.3 C100H: C8DC3-Hydroxy-decanoyl carnitine or Suberoyl carnitine 0.91 6.47 C12: 1Dodecenoyl carnitine 0.91 8.07 C12 Lauroyl carnitine 0.48 12.54 C12OH:C10DC 3-Hydroxy-dodecanoyl carnitine or Sebacoyl 0.94 3.16 carnitineC14: 2 Tetradecadienoyl carnitine 0.89 4.25 C14: 1 Tetradecenoylcarnitine 0.74 4.62 C14: 1-OH 3-Hydroxy-tetradecenoyl carnitine 0.74 4.8C14-OH: 12-DC 3-Hydroxy-tetradecanoyl carnitine or 0.68 4.91Dodecanedioyl carnitine C16 Palmitoyl carnitine 0.36 7.83 C16-OH: C14-DC3-Hydroxy-hexadecanoyl carnitine or 0.95 3.98 Tetradecanedioyl carnitineC16: 2 Hexadecadienoyl carnitine NA NA C16: 1 Palmitoleoyl carnitine NANA C16: 1-OH/ 3-Hydroxy-palmitoleoyl carnitine or NA NA C14: 1-DCcis-5-Tetradecenedioyl carnitine C18: 2 Linoleyl carnitine 0.84 4.33C18: 1 Oleyl carnitine 0.90 4.77 C18 Stearoyl carnitine 0.53 6.08 C18:2-OH 3-Hydroxy-linoleyl carnitine NA NA C18: 1-OH 3-Hydroxy-octadecenoylcarnitine NA NA C18-OH: C16-DC 3-Hydroxy-octadecanoyl carnitine or 0.707.91 Hexadecanedioyl carnitine, thapsoyl carnitine C20 Arachidoylcarnitine, eicosanoyl carnitine 0.65 4.49 C20: 1-OH/ Octadecenedioylcarnitine 0.66 8.46 C18: 1-DC C20-OH/ 3-Hydroxy-eicosanoyl carnitine orOctadecanedioyl 0.39 9.42 C18-DC carnitine C20: 4 Arachidonoyl carnitineNA NA C22 Behenoyl carnitine, docosanoyl carnitine 0.84 5.16 GLY Glycine0.85 1.36 ALA Alanine 0.97 0.49 SER Serine 0.79 2.14 PRO Proline 0.681.11 VAL Valine 0.84 1.25 LEU/ILE Leucine/lsoleucine 0.95 0.79 METMethionine 0.95 1.45 HIS Histidine 0.33 2.04 PHE Phenylalanine 0.95 0.72TYR Tyrosine 0.94 1.61 ASX Aspartic acid/asparagine 0.88 1.55 GLXGlutamine/glutamate 0.55 2.33 ORN Ornithine 0.86 1.54 CIT Citrulline0.57 4.69 ARG Arginine 0.98 1.37 FFA Total free fatty acids 0.99 14.1HBUT β-hydroxybutyrate 0.90 4.8 KET Ketones 0.91 3.7 Metabolite shortnames, full names, and measures of intra-individual variability arepresented.

TABLE 2 Biological functions of the metabolites measured. By measuringconventional metabolites such as fatty acids, ketones andbeta-hydroxybutyrate, a large panel of acylcarnitines and fifteen of thebiologically relevant amino acids, the panel of metabolites surveyedreport on the major pathways of lipid, protein and carbohydratemetabolism. This table displays biological functions (when available) ofeach of the individual metabolites measured. Biological functionBranched β- ω- chain oxidation oxidataion Cholesterol Canonical 20 aminoGlycolysis of of & chol amino acids of Urea acid & Krebs OtherMetabolite fatty acids fatty acids Ketogenesis transport polypeptidescycle catabolism Cycle function(s)/related conditions C2 x C3 x ? xPropionyl-CoA carboxylase deficiency C4:C14 x x C5:1 x C5 x ? x C4-OH xx C14-DC:C4DC x x x C8:1 x C8 x C8:1-OH/C6:1- x x DC C8:1-DC x x C5-DC x? Glutaric acidermia type1 unable to break down completely the aminoacids lyase, hydroxylysine and tryptophan. C6-DC x 3-methylglutarylcarnitine_HMGCo4 lyase deficiency C10:3 x C10:1 x C10 x C10OH:C8DC x xC12:1 x C12 x C12OH:C10DC x x C14:2 x C14:1 x C14:1-OH C14-OH:12-DC x xC16 x C16-OH:C14-DC x x C16:2 x C16:1 x C16:1-OH/C14:1- x x DC C16:2 xC16:1 x C16 x C16:2-OH x C18:1-OH x C18-OH:C16-DC x x C20 xC20:1-OH/C18:1- x x DC C20-OH/C18-DC x x C20:4 x x Eicosanoids C22 x GLYx Neurotransmitter, glutathione ALA x Transaminstiors SER x Purines,pyrimicines PRO x VAL x x LEU/ILE x x MET x Carnitine, taurine, 1-carbonmetabolism. phospholipids, homocysteine HIS x Histamine PHE xNeurotransmitters TYR x Neurotransmitters melanin ASX x xNeurotransmitters, nitrogen transport GLX x x Transaminations, nitrogentransport, glutathione, neurotransmitters ORN x Polyamines CIT x ARG x xNitric oxide FFA x x HBUT x KET x

Methods of predicting the risk of a cardiovascular event (death ormyocardial infarction) in a subject by detecting at least one metabolitein the subject are also provided. The metabolites predictive of risk ofa cardiovascular event are presumed products of peroxisomal fatty acidmetabolism, in particular the short-chain dicarboxyl acylcarnitines, andcitrulline. The specific metabolites are listed in Table 9 of theExamples and are identified as factor 8. Table 10 shows the individualmetabolites within Factor 8 and provides the Factor load data for eachmetabolite. The data in the Examples demonstrate that citrulline and theshort-chain dicarboxyl acylcarnitines are predictive of the risk of acardiovascular event.

Individual metabolites may also be predictive of the risk of acardiovascular event. These metabolites include Gly, Ala, Ser, Pro, Met,His, Phe, Tyr, Asx, Glx, Ornithine, Citrulline, arg, C2, C3, C4:C14;C5:1, C5, C4:OH, C14-DC:C4DC, C5-DC, C6-DC, C10:3, C10, C10-OH:C8DC,C12:1, C12, C12-OH:C10DC, C14:1-OH, C14-OH:C12-DC, C16, C16-OH/C14-DC,C18:2, C18-OH/C16-DC, C20, C20:1-OH/C18:1-DC, C20-OH/C18-DC,C8:1-OH/C6:1-DC, C8:1-DC, C16:1, C16:1-OH/C14:1-DC, C20:4, FFA, HBUT,and Ket. In particular, the levels of citrulline, C5-DC, C6-DC,C8:1-OH/C6:1-DC, and C8:1-DC are predictive of cardiovascular events. Inaddition, the levels of ornithine, citrulline, C5, C14-DC:C4DC, C5-DC,C6-DC, C10-OH:C8DC, C8:1-OH/C6:1-DC, C8:1-DC, C20:4 and FFA are alsouseful for assessing the risk of a cardiovascular event.

Methods of assessing the presence and/or extent of CAD in a subject bydetecting the level of at least one metabolite in a sample from thesubject are also provided. The metabolites useful for assessing thepresence of CAD are medium-chain acylcarnitine, a branched chain aminoacid or associated metabolite, or a metabolite associated with the ureacycle.

The specific metabolites are listed in Table 6, 7 and 8 of the Examplesand are identified as factors 1, 4, and 9 in Table 9. Table 10 shows theindividual metabolites within Factors 1, 4 and 9 and provides the Factorload data for each metabolite. Only those metabolites with factor loadsgreater than or equal to 0.04 are included in the factor.

Individual metabolites may also be predictive of the risk of acardiovascular event. These metabolites include Pro, Leu/Ile, Met, Val,Glx, Citrulline, C2, C3, C4:Ci4; C5, C8, C8:1-OH/C6:1-DC, C10:1, C14:2,C14:1-OH, C16:2, C16:1, C16:2, C16:1, C16:1-OH/C14:1-DC, C18-OH/C16-DC,HBUT, and Ket. In particular, the levels of Leu/Ile, Glx, C2, C14:1-OHand C16:1-OH/C14:1-DC are indicative of the presence of CAD in asubject. Increased levels of Leu/Ile or Glx as compared to normalcontrols or a normal standard are indicative of CAD in the subject.Decreases levels of C2, C14:1-OH and C16:1-OH/C14:1-DC are indicative ofthe presence of CAD in a subject. A level of Leu/Ile greater than 165mM, 170 mM or 175 mM is indicative of coronary artery disease. A levelof Glx greater than 127 mM, 128 mM, 129 mM, 130 mM, 132 mM, 135 mM or140 mM is indicative of coronary artery disease. A level of C14:1-OHless than 0.014 μM, 0.013 μM or 0.012 μM is indicative of coronaryartery disease. A level of C16:1-OH/C14:1-DC less than 0.009 μM, 0.0089μM, or 0.0088 μM is indicative of coronary artery disease.

Methods of assessing the likelihood of developing CAD in a subject bydetecting the level of at least one metabolite in a sample from thesubject are also provided. The metabolites useful for assessing thelikely development of CAD are the short- and medium-chain acylcarnitinemetabolites, branched chain amino acids and urea cycle relatedmetabolites.

The specific metabolites are listed in Table 14 of the Examples. Table15 shows the individual metabolites within the identified Factors. Onlythose metabolites with factor loads greater than or equal to 0.04 areincluded in the factor. Individual metabolites may also be predictive ofthe risk of a cardiovascular event. These metabolites include ketones,arg, ornithine, citrulline, glx, ala, val, leu/Ile, pro, C2, C14:1,C18:1, C5:1, C4-i4, C18, C10:1 and FFA.

The Examples below are meant to be illustrative and not to limit thescope of the invention.

EXAMPLES Example 1 Association of Metabolites with CAD and Risk ofCardiovascular Events Methods Study Sample

The CATHGEN biorepository consists of subjects recruited sequentiallythrough the cardiac catheterization laboratories at Duke UniversityMedical Center (Durham, N.C.). After informed consent, blood wasobtained from the femoral artery at time of arterial access forcatheterization, immediately processed to separate plasma, and frozen at−80° C. All subjects were fasting for a minimum of six hours prior tocollection. Clinical data were provided by the DDCD, a database ofpatients undergoing cardiac catheterization at Duke University since1969. Medication data were collected for medications used chronically,i.e. medications at admission (inpatients) or from a clinic note withinone month prior (outpatients). Follow-up data, including occurrence ofmyocardial infarction (MI) and death were collected at six months aftercatheterization, then annually thereafter. Vital status was confirmedthrough the National Death Index. The indication for catheterization forall subjects was clinical concern for ischemic heart disease. Patientswith severe pulmonary hypertension or organ transplant were excluded.

To evaluate the discriminative capability of metabolites for CAD, twoindependent case-control groups were constructed: ‘initial’ (174 CADcases and 174 CAD-free controls); and ‘replication’ (140 CAD cases and140 CAD-free controls). For the initial group, sequential cases meetinginclusion criteria were selected: CADindex ≧32 (at least one coronaryartery with ≧95% stenosis) and age-of-onset ≦55 years. CADindex is anumerical summary of angiographic data. (Smith et al., Circulation 1991;84[5 Suppl], 111:245-253.) Age-of-onset was defined as age at first MI,percutaneous coronary intervention (PCI), coronary artery bypassgrafting (CABG), or age at first catheterization meeting CADindexthreshold. Sex- and race-matched controls meeting the following criteriawere selected: CADindex ≦23; no coronary artery with >50% stenosis;age-at-catheterization ≧61 years; and no history of MI, PCI, CABG, ortransplant. Given the differences in age based on these criteria,results could be confounded by age. Therefore, for the replicationgroup, sequential cases and controls meeting the same inclusion criteriawere selected, but the criterion of age-of-onset (cases) orage-at-catheterization (controls) was removed and cases/controls werenot matched. This allowed generalizability of findings to arepresentative population of patients referred for catheterization.Analyses were also performed by constraining CAD cases to those with aprevious history of MI (N=86 cases in initial, N=61 cases inreplication).

To evaluate the capability of metabolites to predict risk of subsequentcardiovascular events, an ‘event’ group was constructed, combining CADcases from the initial and replication groups (‘event’ group, N=314); ofthese, 74 individuals suffered death or MI during follow-up. To validatefindings for the association of metabolites with risk of cardiovascularevents, profiling was performed in an independent cardiovascular eventcase-control group (event-replication') composed of unique individualsfrom CATHGEN meeting the following criteria: ejection fraction >40%; nohistory of PCI or CABG; and no subsequent CABG. Among these, event cases(N=63) suffered death or MI, or had PCI with acute coronary syndromewithin two years after catheterization; controls (N=66) were event-free,with at least two years of follow-up, and were matched to cases on age,race, sex and CADindex.

The Duke Institutional Review Board approved the protocols for CATHGENand the current study. Informed consent was obtained from each subject.

Metabolite Measurements

Fasting plasma samples were used for quantitative determination oftargeted levels for 45 acylcarnitines, 15 amino acids, five conventionalanalytes (total, low-density[LDL] and high-density lipoprotein [HDL]cholesterol, triglycerides and glucose), ketones, β-hydroxybutyrate,total free fatty acids and C-reactive protein [CRP] (Table 1).Methodology and coefficients of variation for each assay have beenreported. (Shah et al., Mol Syst Biol 2009; 5:258 and Newgard et al.,Cell Metab 2009; 9(4):311-26.) The laboratory (Sarah W. StedmanNutrition and Metabolism metabolomics/biomarker core laboratory) wasblinded to case-control status and cases/controls were randomlydistributed.

Standard clinical chemistry methods were used for conventionalmetabolites with reagents from Roche Diagnostics (Indianapolis, Ind.),and for free fatty acids (total) and ketones (total andβ-hydroxybutyrate) with reagents from Wako. All assays were performed ona Hitachi 911 clinical chemistry analyzer.

For mass spectroscopy (MS)-profiled metabolites (acylcarnitines, aminoacids) the following protocol was used. (An et al., Nat Med 2004;10(3):268-74 and Chace et al., Clin Chem 1995; 41(1):62-8.) Proteinswere first removed by precipitation with methanol. Aliquotedsupernatants were dried, and then esterified with hot, acidic methanol(acylcarnitines) or n-butanol (amino acids). Analysis was done usingtandem MS with a Quattro Micro instrument (Waters Corporation, Milford,Mass.). Quantification of the “targeted” intermediary metabolites wasfacilitated by addition of mixtures of known quantities ofstable-isotope internal standards (Table 3). Leucine/isoleucine(LEU/ILE) are reported as a single analyte because they are not resolvedby our MS/MS method, and include contributions from allo-isoleucine andhydroxyproline. Under normal circumstances these isobaric amino acidscontribute little to the signal attributed to LEU/ILE. In addition, theacidic conditions used to form butyl esters results in partialhydrolysis of glutamine to glutamic acid and of asparagine to aspartate.Accordingly, values that are reported as GLU/GLN (GLX) or ASP/ASN (ASX)are not meant to signify the molar sum of glutamate and glutamine, or ofaspartate and asparagine, but rather measure the amount of glutamate oraspartate plus the contribution of the partial hydrolysis reactions ofglutamine and asparagine, respectively. Biological annotation isincluded in Table 2 above.

TABLE 3 Internal spiked standards for acylcarnitine and amino acidmeasurements. Amino Acids Acylcarnitines Free Fatty Acids¹⁵N₁,¹³C₁-glycine D₃-acetyl carnitine D₃-octanoate D₄-alanineD₃-propionyl carnitine D₃-decanoate D₈-valine D₃-butyryl carnitineD₃-laurate D₇-proline D₉-isovaleryl carnitine D₃-myristate D₃-serineD₃-octanoyl carnitine D₃-palmitate D₃-leucine D₃-palmitoyl carnitine¹³C₁-oleate D₃-methionine D₃-stearate D₅-phenylalanine D₄-tyrosineD₃-aspartate D₃-glutamate D₂-ornithine D₂-citrulline D₅-arginine

Statistical Analysis

Metabolite levels reported as “0” (i.e., below the lower limits ofquantification (LOQ)) were given a value of LOQ/2. Metabolites with >25%of values as “0” were not analyzed (five acylcarnitines). Allmetabolites were natural log-transformed to approximate a normaldistribution. For analysis of CAD status, generalized linear regressionmodels were used to assess differences in metabolite levels between CADcases and controls, both unadjusted and adjusted for traditional CADrisk factors not constrained by matching: diabetes, hypertension,dyslipidemia, body-mass-index (BMI), family history of CAD, and smokingAnalyses of the replication group were further adjusted for race, sexand age. With log transformation, all significant metabolites showed anormal distribution (Kolmogorov-Smirnov test P>0.01), except valine,ketones, and C8, C8:1-OH/C6:1-DC, C10:1, C14:2, C16:1,C16:1-OH/C14:1-DC, and C18-OH/C16-DC acylcarnitines. Visual inspectionof the distributions suggested a grossly normal distribution.Regardless, we performed sensitivity analyses using non-parametricWilcoxon tests, showing similar results as the semi-parametric linearmodels, except for valine and C14:2 acylcarnitine, both of which werenot significant in linear regressions (p=0.10 and p=0.06, respectively),but were significant with these non-parametric tests (p=0.05 andp=0.008). Analyses were also stratified by diabetes and smoking.

In exploratory analyses, multivariable models were further adjusted formedication classes (beta-blockers, statins, diabetes medications,aspirin, angiotensin-converting-enzyme inhibitors, nitrates, clopidogreland diuretics), use of pre-procedural sedation, and continuousintravenous heparin use at time of catheterization. The CATHGEN protocolrequires sample collection prior to supplemental heparin administrationduring catheterization. Therefore, adjustment for continuous intravenousheparin use at time of catheterization addresses differences related toheparin. Only 66% of individuals had medication data, hence medicationswere coded as a discrete variable: not on medication, missing, and onmedication.

Given that metabolites reside in overlapping pathways, correlation ofmetabolites is expected. We used principal components analysis (PCA) toreduce the large number of correlated variables into uncorrelatedfactors. Factors with higher “eigenvalues” account for larger amounts ofvariability within the dataset. Factors with an eigenvalue ≧1.0 wereidentified and varimax rotation performed to produce interpretablefactors. Metabolites with a factor load ≧|0.4| were reported ascomposing a factor. See Table 10. Scoring coefficients were constructedfrom the initial group and used to calculate factor scores for eachindividual (weighted sum of the standardized metabolites within thatfactor, weighted on the factor loading for each metabolite), and werealso applied to the replication group. Generalized linear regressionmodels were used to assess the difference in factor scores between casesand controls. All factors were normally distributed (Kolmogorov-Smirnovtest P>0.01), except for factors 7-9; visual inspection showed a grosslynormal distribution. Non-parametric Wilcoxon tests for these factorsshowed the same results as linear models.

To further assess the independent capability of metabolite profiles todiscriminate CAD cases from controls, multivariable logistic regressionmodels were constructed; in these models, CAD risk factors (BMI,dyslipidemia, hypertension, diabetes, family history, smoking) wereforced into the model, then metabolite factors were added. Receiveroperating curves (ROC) were constructed and measures of model fitcalculated. Nonparametric analysis for comparison of the areas underthese curves was performed using previous methods. (DeLong et al.,Biometrics 1988; 44(3):837-45.)

For analysis of subsequent cardiovascular events, cases from initial andreplication groups were pooled (‘event’ group). The relationship betweenmetabolite factors and time-to-occurrence of death/MI was assessed usingCox proportional hazards (unadjusted and adjusted for BMI, dyslipidemia,hypertension, diabetes, family history, smoking, age, race, sex,creatinine, ejection fraction and CADindex). The assumption ofproportional hazards was met. For replication in the ‘event-replication’group, scoring coefficients from PCA-derived factors constructed in theinitial CAD group were used to calculate factor scores in theevent-replication group; logistic regression was used to assess theassociation between factors and case/control status (unadjusted andadjusted for BMI, dyslipidemia, hypertension, diabetes, family history,smoking, creatinine, and ejection fraction).

As all analyses were exploratory in nature and given co-linearity of themetabolites, two-sided p-values unadjusted for multiple comparisons arepresented; however, results interpreted in the context of Bonferronicorrection are reported. Nominal statistical significance was defined asP<0.05. Bonferroni corrected p-values were P<0.0007 (individualmetabolites) and P<0.004 (factors). Statistical analyses were performedby D.R.C. and S.H.S. using SAS version 9.1 (Cary N.C.).

Results Patient Populations

Population characteristics for the initial (174 early-onset CAD cases,174 matched controls) and replication groups (140 CAD cases, 140controls) are displayed in Table 4, and for the event-replication groupin Table 5.

TABLE 4 Baseline Clinical Characteristics of Initial and ReplicationGroups. Initial Group Replication Group Cases Controls Cases Controls (N= 174) (N = 174) (N = 140) (N = 140) Age (mean [SD]) 48.7 (10.0) 67.8(5.9) 61.1 (13.0) 60.3 (13.0) Age-of-onset 45.8 (6.9) N/A 57.0 (10.8)N/A Sex (% male) 77.0% 75.3% 75.0% 51.4% Race (% white) 66.9% 67.8%77.5% 76.9% Hypertension 64.9% 68.4% 72.9% 64.3% Diabetes 32.2% 23.0%28.6% 19.3% Family history of CAD 57.5% 22.4% 49.3% 32.9% Currentlysmoking (%) 66.1% 47.1% 64.3% 39.3% Body mass index 31.1 (6.8) 29.3(6.0) 29.1 (5.8) 31.4 (9.1) (mean [SD]) CADindex (mean [SD]) 56.5 (21.9)6.2 (9.4) 58.6 (21.8) 4.6 (8.6) No. of coronary arteries w/≧75% stenosis0   0%  100%   0% 100% 1 27.0%   0% 22.1%   0% 2 29.3%   0% 35.0%   0% 343.7%   0% 42.9%   0% Ejection fraction (mean 51.8 (13.0) 59.4 (13.0)53.5 (15.1) 63.4 (9.5) [SD]) History of MI 49.4%   0% 42.9%   0% Historyof dyslipidemia 73.6% 44.8% 60.7% 49.3% Total cholesterol (mean 178.5(55.1) 176.1 (39.2) 177.9 (44.4) 169.9 (38.7) [SD]) LDL cholesterol(mean 105.3 (39.3) 104.2 (32.1) 105.9 (36.8) 101.1 (32.5) [SD]) HDLcholesterol (mean 35.6 (10.8) 48.0 (16.0) 39.3 (12.2) 39.0 (12.6) [SD])Triglycerides (mean 157.7 (170.4) 93.2 (60.7) 128.1 (82.4) 119.8 (91.1)[SD])

TABLE 5 Baseline Clinical Characteristics of the Event ReplicationGroup. Overall Event Cases No Event Controls (N = 129) (N = 63) (N = 66)P-value* Age (mean [SD)) 62.9 (10.4) 63.2 (10.7) 62.7 (10.1) 0.81 Sex (%male) 54.3% 54.0% 54.6% 0.95 Race (% white) 74.2% 72.1% 76.2% 0.57Hypertension 72.1% 77.8% 66.7% 0.16 Diabetes 31.8% 33.3% 30.3% 0.71Family History of CAD 32.6% 27.0% 37.9% 0.19 Currently smoking (%) 51.2%44.4% 53.0% 0.33 Body mass index 30.0 (7.6) 30.5 (8.5) 29.4 (6.6) 0.42(mean [SD]) CADindex (mean [SD]) 28.6 (12.4) 28.9 (12.0) 26.3 (12.8)0.81 No. of coronary arteries 0.96 w/≧75% stenosis 0 15.1% 15.0% 15.2% 163.5% 63.3% 63.6% 2 19.1% 20.0% 18.2% 3  2.4%  1.7%  3.0% Ejectionfraction 61.0 (9.2) 61.8 (8.8) 0.63 (mean [SD]) History of MI   0%   0%NA History of dyslipidemia 55.6% 54.6% 0.91 Total cholesterol 166.6(44.2) 168.7 (49.0) 0.81 (mean [SD]) LDL cholesterol (mean 104.5 (34.2)108.0 (28.6) 0.70 [SD]) HDL cholesterol (mean 49.0 (16.4) 51.6 (22.2)0.53 [SD]) Triglycerides (mean 117.0 (94.9) 108.5 (113.9) 0.36 [SD])*p-value for difference between subjects with events and event-freecontrols.Association of Individual Metabolites with CAD

Levels of several amino acids were different between cases and controlsin the initial group (Table 6), including the branched-chain amino acidsleucine/isoleucine (P<0.0001) and valine (P=0.007), glutamate/glutamine(P<0.0001), proline (P=0.04) and methionine (P=0.05). Levels of severalacylcarnitines were also different between cases and controls in theinitial group, including the C16 acylcarnitines (C16:1, P=0.006;C16:1-OH/C14:1-DC, P=0.004; C16:2, P=0.05; and C18-OH/C16-DC, P=0.003),and C4:Ci4 (P=0.009), C8 (P=0.009), C8:1-OH/C6:1-DC (P=0.003), and C10:1(P=0.002) acylcarnitine (Table 6). For most metabolites, thesedifferences persisted after adjustment for CAD risk factors.

TABLE 6 Association of Individual Metabolites with CAD. Means andstandard deviations for metabolites significantly different betweencases and controls in the initial group are presented. Results for theseanalytes in the replication group are also presented. All values are inmillimolar for amino acids and micromolar for acylcarnitines. Analytesin bold show consistent association across both datasets (withconsistent direction of effect). Initial Group Replication GroupMetabolite CAD Cases Controls Unadj p Adj p* Cases Controls Unadj p Adjp† Amino Acids PRO 190.2 (56.4)  177.5 (41.3)  0.04 0.13 197.0 173.90.001 0.03 (75.9) (44.3) LEU/ILE 175.1 (39.5)  158.7 (36.3)  <0.00010.004 183.6 162.3 <0.0001 0.002 (52.7) (35.5) VAL 259.1 (58.1)  242.2(54.9)  0.007 0.26 256.8 266.2 0.10 0.05 (63.7) (51.5) MET 25.8 (5.6) 24.6 (4.8)  0.05 0.14 26.6 24.0 0.003 0.03 (7.7) (5.2) GLX 151.1 (41.7) 125.5 (39.0)  <0.0001 <0.0001 129.7 120.3 0.005 0.02 (30.5) (31.4) CIT36.4 (12.2) 39.9 (11.2) 0.002 0.003 39.7 37.8 0.21 0.86 (12.0) (10.8)Acylcarnitines C2 9.10 (4.25) 9.90 (3.98) 0.02 0.01 10.73 8.76 <0.0001<0.0001 (4.86) (3.83) C4:Ci4 0.22 (0.12) 0.18 (0.10) 0.009 0.03 0.220.20 0.06 0.15 (0.11) (0.11) C5 0.104 (0.095) 0.087 (0.047) 0.01 0.080.092 0.101 0.05 0.004 (0.045) (0.045) C8 0.107 (0.057) 0.123 (0.076)0.009 0.04 0.129 0.124 0.54 0.17 (0.121) (0.106) C8:1-OH/C6:1- 0.030(0.027) 0.032 (0.019) 0.003 0.005 0.026 0.028 0.47 0.56 DC (0.013)(0.012) C10:1 0.174 (0.097) 0.193 (0.083) 0.002 0.002 0.200 0.198 0.870.47 (0.096) (0.096) C14:2 0.039 (0.027) 0.044 (0.028) 0.02 0.02 0.0390.047 0.06 0.42 (0.028) (0.025) C14:1-OH 0.012 (0.006) 0.014 (0.007)0.04 0.03 0.012 0.015 0.002 0.006 (0.006) (0.007) C16:2 0.0088 (0.0065)0.0092 (0.0053) 0.05 0.03 0.0098 0.0117 0.03 0.11 (0.0059) (0.007) C16:10.0258 (0.0171) 0.0262 (0.0124) 0.006 0.07 0.0286 0.0321 0.03 0.24(0.0154) (0.0138) C16:1- 0.0087 (0.0036) 0.0091 (0.0031) 0.004 0.010.0088 0.0096 0.008 0.01 OH/C14:1-DC (0.0040) (0.0040) C18-OH/C16- 0.008(0.009) 0.007 (0.004) 0.003 0.02 0.007 0.008 0.005 0.03 DC (0.003)(0.004) Ketones 289.8 (345.0) 324.3 (286.0) 0.04 0.14 319.2 313.1 0.870.44 (289.9) (279.0) β- 199.7 (271.6) 237.1 (235.3) 0.01 0.05 211.5202.9 0.63 0.29 hydroxybutyrate (205.7) (199.8) *adjusted for diabetes,hypertension, smoking, dyslipidemia, family history of CAD, BMI.†adjusted for age, race, sex, diabetes, hypertension, smoking,dyslipidemia, family history of CAD, BMI

Several of these metabolites were also significant in the replicationgroup in adjusted analyses (with similar direction of effect), includingthe amino acids leucine/isoleucine and glutamate/glutamine, and thelong-chain acylcarnitines C14:1-OH and C16:1-OH/C14:1-DC (Table 6). Inunadjusted analyses, these metabolites, amino acids methionine andproline, and C16:2 and C16:1 acylcarnitine were significant in bothgroups.

Further adjustment for lipids (total, LDL, and HDL cholesterol andtriglycerides) resulted in similar results, although with attenuation ofassociation for LEU/ILE in the initial group (Tables 7 and 8). Analysesstratified by diabetes suggested some heterogeneity of association bydiabetes. For example, LEU/ILE and C16:1-OH/C14:1-DC showed strongerassociation in non-diabetics. Analyses stratified by smoking suggestedno difference in smokers and non-smokers.

TABLE 7 Association of Individual Metabolites with CAD in the InitialGroup. Means and standard deviations for all individual metabolites forthe overall initial group, as well as stratified by CAD, are presented.All values are in millimolar for amino acids and micromolar foracylcarnitines. P-values are for the association between metabolites andCAD in unadjusted and adjusted analyses. Analytes in bold showconsistent association across both datasets in adjusted analyses (withconsistent direction of effect). Overall CAD Cases CAD Controls Mean SDMean SD Mean SD Unadjusted Adjusted* Adjusted p† Amino Acids GLY 311.4877.87 308.33 78.16 316.65 79.53 0.23 0.57 0.20 ALA 324.98 87.49 333.5694.24 316.19 79.76 0.12 0.51 0.37 SER 93.59 20.53 100.16 20.30 97.2120.64 0.15 0.09 0.39 PRO 183.78 49.65 190.17 56.43 177.58 41.28 0.040.13 0.56 VAL 250.59 57.02 259.08 58.33 242.16 54.94 0.007 0.26 0.84LEU/ILE 166.94 38.70 175.12 39.47 158.74 36.32 <0.0001 0.004 0.30 MET25.17 5.26 25.75 5.58 24.56 4.83 0.05 0.14 0.92 HIS 60.95 13.09 60.5713.76 61.10 12.43 0.71 0.63 0.30 PHE 65.36 12.93 66.57 13.28 64.12 12.520.09 0.29 0.60 TYR 61.65 15.05 62.56 15.72 60.95 14.18 0.37 0.47 0.16ASX 108.58 23.06 110.70 23.54 108.89 22.30 0.12 0.54 0.94 GLX 138.1742.23 151.07 41.68 125.46 39.02 <0.0001 <0.0001 0.02 ORN 75.14 23.3177.53 24.08 72.75 22.41 0.08 0.15 0.92 CIT 38.23 11.85 38.44 12.17 39.8611.21 0.002 0.003 0.003 ARG 70.54 20.74 71.25 22.16 89.58 19.01 0.840.29 0.86 Acylcamitines C2 9.499 4.123 9.105 4.249 9.900 3.976 0.02 0.010.01 C3 0.469 0.243 0.496 0.270 0.438 0.210 0.06 0.63 0.72 C4:C14 0.2000.113 0.216 0.116 0.183 0.104 0.009 0.03 0.81 C5.1 0.057 0.034 0.0570.035 0.056 0.031 0.90 0.62 0.97 C5 0.096 0.075 0.104 0.094 0.067 0.0470.01 0.08 0.88 C4:OH 0.056 0.039 0.058 0.045 0.054 0.032 0.83 0.62 0.70C14-DC:C4DC 0.052 0.033 0.055 0.039 0.049 0.024 0.53 0.48 0.43 C8:10.262 0.145 0.271 0.164 0.253 0.123 0.69 0.50 0.12 C8 0.115 0.068 0.1070.067 0.123 0.076 0.009 0.04 0.04 C5-DC 0.044 0.045 0.045 0.062 0.0430.019 0.29 0.53 0.15 C6-DC 0.085 0.131 0.084 0.181 0.075 0.042 0.86 0.730.07 C10:3 0.125 0.076 0.125 0.063 0.125 0.067 0.37 0.24 0.11 C10:10.154 0.090 0.174 0.095 0.193 0.083 0.002 0.002 0.0002 C10 0.223 0.1640.200 0.154 0.245 0.207 0.05 0.14 0.00 C10-OH:C8DC 0.035 0.026 0.0340.028 0.035 0.023 0.11 0.37 0.11 C12:1 0.120 0.069 0.117 0.075 0.1220.062 0.12 0.48 0.05 C12 0.075 0.053 0.074 0.059 0.075 0.047 0.32 0.840.11 C12-OH:C10DC 0.009 0.007 0.008 0.007 0.009 0.007 0.10 0.23 0.07C14:2 0.043 0.026 0.041 0.025 0.046 0.027 0.02 0.02 0.01 C14:1 0.0510.050 0.078 0.049 0.085 0.051 0.53 0.07 0.05 C14:1-OH 0.014 0.007 0.0130.006 0.015 0.007 0.04 0.03 0.009 C14-OH/C12-DC 0.010 0.005 0.010 0.0060.010 0.005 0.78 0.20 0.10 C16 0.086 0.025 0.087 0.026 0.085 0.024 0.590.43 0.98 C16-OH/C14-DC 0.007 0.005 0.006 0.005 0.007 0.005 0.40 0.250.07 C18:2 0.074 0.038 0.072 0.038 0.075 0.039 0.40 0.24 0.21 C18:10.154 0.063 0.150 0.058 0.157 0.069 0.30 0.32 0.24 C18 0.044 0.015 0.0430.014 0.046 0.015 0.22 0.34 0.25 C18:1-OH/C16:1-DC 0.010 0.006 0.0100.006 0.010 0.007 0.46 0.04 0.03 C18-OH/C16-DC 0.009 0.008 0.010 0.0090.008 0.005 0.003 0.02 0.09 C20 0.009 0.007 0.008 0.007 0.009 0.009 0.220.20 0.13 C20:1-OH/C18:1-DC 0.010 0.009 0.011 0.011 0.009 0.009 0.650.54 0.89 C20-OH/C18-DC 0.010 0.010 0.011 0.014 0.009 0.005 0.26 0.760.92 C22 0.009 0.008 0.009 0.008 0.006 0.007 0.24 0.69 0.00C8:1-OH/C6:1-DC 0.031 0.021 0.029 0.025 0.032 0.017 0.003 0.005 <.0001C8:1-DC 0.029 0.022 0.029 0.027 0.026 0.014 0.24 0.97 0.23 C16:2 0.0120.007 0.012 0.007 0.013 0.009 0.05 0.03 0.09 C16:1 0.029 0.015 0.0270.016 0.030 0.015 0.006 0.07 0.05 C16:1-OH/C14:1-DC 0.010 0.054 0.0090.005 0.010 0.004 0.004 0.01 0.003 C18:2-OH 0.014 0.009 0.014 0.0080.014 0.006 0.82 0.42 0.81 C20:4 0.011 0.007 0.011 0.007 0.011 0.0060.91 0.92 0.78 Other Total free fatty acids 1.13 0.59 1.09 0.61 1.180.56 0.11 0.08 0.28 Ketones 306.49 315.4 289.82 345.01 324.29 285.960.04 0.14 0.73 β-hydroxybutyrate 218.01 254.14 199.72 271.84 237.06235.30 0.01 0.05 0.54 *adjusted for diabetes, hypertension, smoking,dyslipidemia, family history of CAD, BMI. †adjusted for diabetes,hypertension, smoking, dyslipidemia, family history of CAD, BMI, andtotal, LDL, HDL cholesterol and triglycerides.

TABLE 8 Association of Individual Metabolites with CAD in theReplication Group. Means and standard deviations for all individualmetabolites for the overall replication group, as well as stratified byCAD, are presented. All values are in millimolar for amino acids andmicromolar for acylcarnitines. P-values are for the association betweenmetabolites and CAD in unadjusted and adjusted analyses. Analytes inbold show consistent association across both datasets in adjustedanalyses (with consistent direction of effect). Overall CAD Cases CADcontrols P-values Mean SD Mean SD Mean SD Unadjusted Adjusted* Adjustedp† Amino Acids GLY 315.81 88.31 320.30 73.59 311.33 82.54 0.34 0.58 0.57ALA 322.45 101.97 338.60 112.14 314.31 90.34 0.25 0.29 0.70 SER 103.6526.15 104.12 29.34 103.18 22.81 0.99 0.52 0.34 PRO 185.45 63.10 197.0475.89 173.67 44.28 0.001 0.03 0.04 VAL 281.47 58.00 258.77 53.88 266.1751.49 0.10 0.05 0.04 LEU/ILE 172.94 46.10 163.63 52.68 152.26 35.50<.0001 0.002 0.002 MET 25.31 8.88 26.58 7.73 24.03 5.16 0.003 0.53 0.04HIS 65.53 13.86 66.25 14.26 67.41 13.47 0.42 0.55 0.14 PHE 66.18 13.4669.20 14.70 63.16 11.40 0.0003 0.002 0.0006 TYR 64.58 15.17 65.72 17.1163.41 12.90 0.39 0.45 0.34 ASX 98.44 26.06 114.16 23.81 82.64 17.67<.0001 <.0001 <.0001 GLX 125.01 31.25 129.72 30.46 128.38 31.43 0.0050.02 0.04 ORN 80.90 23.67 83.07 23.25 78.74 23.96 0.88 0.29 0.46 CIT38.76 11.40 39.74 11.95 37.78 10.77 0.21 0.66 0.86 ARG 72.73 22.56 71.6624.54 73.80 20.42 0.20 0.09 0.14 Acylcarnitines C2 9.743 4.481 10.7264.863 8.756 3.834 <.0001 <.0001 <.0001 C3 0.439 0.230 0.525 0.253 0.3520.164 <.0001 <.0001 <.0001 C4:C14 0.209 0.114 0.222 0.115 0.197 0.1120.05 0.15 0.13 C5:1 0.072 0.030 0.058 0.023 0.087 0.025 <.0001 <.0001<.0001 C5 0.097 0.045 0.092 0.045 0.101 0.044 0.05 0.004 0.003 C4:OH0.068 0.046 0.060 0.038 0.975 0.051 0.006 0.11 0.27 C14-DC:C4DC 0.0560.034 0.065 0.042 0.048 0.019 <.0001 <.0001 <.0001 C8:1 0.267 0.1370.254 0.126 0.279 0.145 0.25 0.53 0.55 C8 0.126 0.113 0.129 0.121 0.1240.106 0.54 0.17 0.08 C5-DC 0.042 0.025 0.044 0.031 0.040 0.019 0.29 0.850.43 C6-DC 0.053 0.080 0.094 0.101 0.552 0.049 0.48 0.62 0.29 C10:30.139 0.062 0.144 0.099 0.133 0.074 0.67 0.46 0.61 C10:1 0.199 0.0960.200 0.099 0.198 0.096 0.87 0.47 0.33 C10 0.272 0.267 0.280 0.307 0.2640.220 0.61 0.94 0.49 C10-OH:C8DC 0.034 0.021 0.036 0.024 0.032 0.0170.15 0.12 0.05 C12:1 0.124 0.059 0.125 0.062 0.123 0.055 0.93 0.66 0.21C12 0.079 0.052 0.066 0.065 0.071 0.033 0.003 0.002 0.0005 C12-OH:C10DC0.008 0.006 0.008 0.005 0.009 0.086 0.05 0.11 0.20 C14:2 0.045 0.0250.043 0.025 0.048 0.025 0.06 0.42 0.51 C14:1 0.087 0.052 0.084 0.0540.090 0.050 0.12 0.53 0.81 C14:1-OH 0.015 0.006 0.013 0.005 0.016 0.0070.002 0.006 0.005 C14-OH/C12-DC 0.011 0.005 0.010 0.006 0.011 0.005 0.300.58 0.53 C16 0.088 0.031 0.089 0.037 0.066 0.022 0.85 0.99 0.20C16-OH/C14-DC 0.007 0.006 0.006 0.006 0.005 0.007 0.006 0.07 0.10 C18:20.074 0.040 0.072 0.048 0.077 0.031 0.05 0.05 0.005 C18:1 0.160 0.0890.157 0.108 0.163 0.064 0.15 0.24 0.05 C18 0.046 0.022 0.845 0.027 0.0470.013 0.03 0.02 <.0001 C18:1-OH/C16:1-DC 0.009 0.005 0.008 0.005 0.0100.005 0.0055 0.01 0.02 C18-OH/C16-DC 0.003 0.005 0.008 0.005 0.009 0.0050.005 0.03 0.009 C20 0.007 0.005 0.007 0.006 0.007 0.004 0.22 0.60 0.89C20:1-OH/C18:1-DC 0.011 0.007 0.011 0.008 0.011 0.006 0.19 0.21 0.37C20-OH/C18-DC 0.009 0.005 0.009 0.006 0.010 0.005 0.02 0.13 0.19 C220.008 0.008 0.009 0.010 0.007 0.006 0.73 0.99 0.88 C8:1-OH/C6:1-DC 0.0260.012 0.027 0.012 0.028 0.012 0.47 0.56 0.67 C8:1-DC 0.027 0.015 0.0290.018 0.025 0.010 0.10 0.06 0.04 C16:2 0.012 0.007 0.011 0.006 0.0130.007 0.03 0.11 0.13 C16:1 0.051 0.014 0.029 0.015 0.032 0.014 0.03 0.240.25 C16:1-OH/C14:1-DC 0.010 0.005 0.009 0.004 0.011 0.004 0.008 0.010.004 C18:2-OH 0.011 0.008 0.012 0.009 0.011 0.007 0.45 0.72 0.50 C20:40.010 0.005 0.010 0.007 0.010 0.005 0.20 0.22 0.12 Other Total freefatty acids 1.16 0.70 1.22 0.79 1.10 0.80 0.26 0.12 0.09 Ketones 316.12283.99 319.20 269.88 313.06 279.02 0.87 0.44 0.10 β-hydroxybutyrate207.21 202.44 211.52 205.69 202.94 199.81 0.63 0.29 0.08 *adjusted forage, race, sex, diabetes, hypertension, smoking, dyslipidemia, familyhistory of CAD, BMI. †adjusted for age, race, sex, diabetes,hypertension, smoking, dyslipidemia, family history of CAD, BMI, andtotal, LDL, HDL cholesterol and triglycerides.

Unbiased Principal Components Analysis

PCA identified 12 factors comprised of collinear metabolites (Table 9),grouping in biologically plausible factors. Three factors weresignificantly different between cases and controls in the initial groupin adjusted analyses: factor 1 (medium-chain acylcarnitines), factor 4(branched-chain amino acids and related metabolites), and factor 9(arginine, histidine, citrulline, Ci4-DC:C4DC). Of these factors, twofactors (4 and 9) remained significant in the replication group. Factor1 was only weakly significant in the replication group (unadjustedP=0.15, adjusted P=0.03). The factor load for each metabolite ispresented in Table 10.

TABLE 9 Principal Components Analysis. Results of unbiased principalcomponents analysis (PCA) are presented. Factors were constructed usingthe initial group; scoring coefficients from this PCA were used tocalculate factor scores for the initial and replication groups. P-valuesfor the difference in the mean value of the factors between cases andcontrols for the initial and replication groups are presented. InitialGroup Replication Group Individual Eigen- CAD MI CAD MI Factor NameComponents* value Var** Unadj Adj† Unadj Adj† Unadj Adj† Unadj Adj† 1Medium Chain C8, C10:1, 12.45 0.21 0.001 0.01 0.01 0.06 0.15 0.03 0.590.18 Acyl-carnitines C12, C10, C12:1, C10- OH:C8DC, C6-DC, C8:1-DC,C14:1, C14:2, C8:1- OH/C6:1- DC, C2 acylcarnitines 2 Long Chain C18:1,5.78 0.10 0.28 0.34 0.21 0.14 0.03 0.01 0.08 0.05 Acyl-carnitines C18:2,C18, C16, C16:1, C20:4, C14:1, C14:2, C16:2, C14:1-OH 3 Long Chain C18-4.75 0.08 0.10 0.36 0.04 0.13 <0.0001 0.004 0.03 0.21 Dicarboxyl/OH/C16- Hydroxyl Acyl- DC, C20- carnitines OH/C18- DC, C20:1- OH/C18:1-DC, C16- OH/C14- DC, C18:1- OH/C16:1- DC, C14- OH/C12- DC, C12- OH:C10-DC, C14:1-OH, C20 4 BCAA Related Phe, Tyr, 2.87 0.05 0.002 0.02 0.00020.01 0.01 0.03 0.006 0.005 leu/Ile, Met, Val, C5, Ala 5 Ketone RelatedKet, Hbut, 2.24 0.04 0.18 0.33 0.02 0.12 0.54 0.41 0.07 0.06 Ala (−),C2, C4:OH, C14:1 6 Various C8:1, 1.92 0.03 0.56 0.75 0.92 0.28 0.79 0.920.76 0.89 C10:3 7 Amino Acids Ser, Gly, 1.71 0.03 0.19 0.13 0.59 0.420.04 0.18 0.28 0.60 FFA (−) 8 Dicarboxyls C5-DC, 1.41 0.02 0.73 0.340.59 0.25 0.05 0.57 0.002 0.04 C8:1- OH/C6:1- DC, Cit, C8:1-DC, C6-DC 9Urea Cycle Arg, His, 1.33 0.02 0.0004 0.004 0.0006 0.01 0.01 0.01 0.0030.006 Related Cit, Ci4- DC:C4DC (−) 10 Short Chain C3, 1.22 0.02 0.020.19 0.03 0.23 0.72 0.92 0.27 0.48 Acyl-carnitines C4:Ci4, C5 11 VariousC5:1, 1.15 0.02 0.62 0.13 0.95 0.13 0.03 0.01 0.13 0.12 C18:2-OH (−),C22 (−) 12 Various Asx, C22 1.08 0.02 0.12 0.83 0.15 0.80 <.0001 <.00010.01 0.05 *Analytes with a factor load ≧|0.4| for that factor arelisted, in order of magnitude of load for that factor; analytes with anegative factor load for that factor are annotated with a (−).**Proportion of variance explained by that factor. †adjusted fordiabetes, hypertension, smoking, dyslipidemia, family history of CAD,BMI; replication group results are additionally adjusted for age, raceand sex.

TABLE 10 Factor Loads of Individual Metabolites on Factors Identifiedfrom PCA on the Initial Group. FACTOR Metabolite 1 2 3 4 5 6 7 8 9 10 1112 GLY 0.25 0.03 −0.16 0.03 −0.06 0.06 0.74 0.12 0.06 −0.08 0.09 0.09ALA 0.09 0.01 0.06 0.43 −0.67 0.13 0.00 −0.16 0.12 0.12 0.08 0.17 SER0.01 0.19 0.02 0.16 0.07 −0.07 0.76 −0.14 0.17 0.04 −0.06 0.09 PRO 0.240.02 −0.17 0.36 −0.37 0.12 0.09 0.17 0.24 0.06 0.05 0.37 VAL −0.24 0.000.02 0.71 0.07 −0.06 −0.12 −0.04 0.06 0.28 −0.11 0.36 LEU/ILE −0.06−0.05 −0.12 0.79 0.15 −0.06 0.09 0.00 −0.08 0.30 0.03 0.19 MET 0.06−0.07 0.00 0.74 −0.14 −0.10 0.28 −0.06 0.24 0.02 0.05 −0.04 HIS −0.130.02 0.08 0.26 −0.13 −0.07 0.28 −0.07 0.58 0.09 −0.21 −0.03 PHE −0.010.06 0.17 0.85 −0.01 −0.02 −0.03 0.07 0.10 0.02 −0.07 −0.03 TYR −0.040.15 0.07 0.80 −0.22 0.10 0.03 −0.06 0.05 −0.05 0.02 −0.13 ASX −0.150.00 0.12 0.07 0.01 −0.08 0.15 0.06 0.05 0.06 0.08 0.69 GLX −0.25 0.110.27 0.29 −0.23 0.28 −0.07 0.08 −0.01 0.06 0.00 0.29 ORN 0.11 0.38 −0.270.38 −0.14 0.14 0.26 0.33 −0.02 0.09 −0.05 0.03 CIT 0.14 0.11 −0.20 0.06−0.06 0.24 0.13 0.46 0.54 −0.08 0.06 0.18 ARG −0.08 −0.19 −0.02 0.26−0.07 −0.09 0.18 0.07 0.68 0.07 0.13 0.04 C2 0.44 0.33 0.08 0.00 0.600.16 0.11 −0.13 0.02 0.22 0.11 0.07 C3 0.00 0.01 −0.16 0.23 −0.15 0.060.04 −0.09 0.11 0.72 0.03 0.05 C4:C14 0.30 −0.06 −0.01 0.19 −0.13 0.140.03 0.18 −0.03 0.50 0.13 0.01 C5:1 0.14 −0.05 −0.07 −0.03 −0.04 −0.090.02 −0.05 0.01 0.30 0.69 0.10 C5 0.13 0.03 0.04 0.44 −0.06 0.06 −0.140.20 −0.01 0.49 0.02 0.10 C4:OH 0.32 0.05 0.07 0.00 0.56 0.24 0.23 −0.07−0.19 0.19 0.29 0.00 C14-DC:C4DC 0.39 −0.01 −0.12 0.18 0.05 0.12 0.220.32 −0.43 0.08 0.06 −0.21 C8:1 0.27 0.04 0.12 0.01 0.06 0.85 −0.02 0.03−0.02 0.10 −0.09 0.05 C8 0.80 0.16 0.15 −0.01 0.10 0.09 0.06 −0.02 0.010.11 −0.13 −0.03 C5-DC 0.34 −0.03 0.28 0.02 −0.04 −0.11 −0.10 0.62 0.110.05 −0.08 0.07 C6-DC 0.60 0.06 0.29 −0.05 0.04 0.14 −0.02 0.41 −0.15−0.03 0.11 0.07 C10:3 0.30 0.07 0.10 −0.04 0.05 0.82 0.01 0.07 −0.040.04 −0.04 −0.10 C10:1 0.76 0.16 0.12 −0.03 0.04 0.31 0.01 0.03 0.000.09 −0.18 −0.04 C10 0.72 0.06 0.00 −0.09 0.14 −0.05 0.09 −0.06 −0.040.03 0.04 0.04 C10-OH:C8DC 0.65 0.16 0.39 0.00 0.19 0.14 −0.08 0.22−0.08 0.03 0.11 −0.01 C12:1 0.88 0.26 0.25 0.10 0.23 0.23 −0.09 0.100.04 −0.06 0.15 −0.11 C12 0.74 0.13 0.00 0.01 0.02 0.03 0.12 0.16 −0.06−0.01 0.24 −0.12 C12-OH:C10DC 0.33 0.09 0.46 −0.02 0.12 0.05 −0.11 0.120.14 −0.07 −0.06 0.20 C14:2 0.47 0.48 0.38 0.04 0.37 0.14 −0.10 0.020.12 −0.07 −0.21 0.00 C14:1 0.52 0.49 0.39 0.02 0.40 0.05 −0.12 −0.010.07 −0.04 −0.10 0.02 C14:1-OH 0.34 0.42 0.44 0.04 0.20 0.11 0.01 0.060.01 0.01 −0.02 −0.08 C14-OH/C12-DC 0.16 0.29 0.51 0.09 −0.03 0.09 0.09−0.10 −0.19 0.01 −0.02 0.12 C16 0.23 0.71 0.30 0.08 0.15 −0.05 0.02−0.15 −0.14 0.03 0.14 0.11 C16-OH/C14-DC 0.18 0.18 0.57 −0.01 0.07 0.00−0.23 −0.09 0.02 0.02 0.05 0.09 C18:2 0.16 0.79 0.16 0.07 0.11 0.16 0.030.05 −0.02 −0.10 −0.22 0.04 C18:1 0.22 0.83 0.24 0.04 0.19 0.01 0.060.00 −0.08 −0.09 −0.03 0.00 C18 0.08 0.75 0.25 −0.07 0.01 −0.02 0.140.01 −0.02 0.08 0.03 0.01 C18:1-OH/C16:1-DC 0.15 0.11 0.52 0.07 0.190.01 −0.07 0.01 −0.01 −0.20 −0.17 0.24 C18-OH/C16-DC −0.05 0.19 0.690.06 0.00 0.09 −0.01 0.08 −0.10 −0.03 −0.10 0.09 C20 −0.04 0.26 0.42−0.07 0.05 0.00 −0.15 0.04 0.34 0.12 −0.07 −0.06 C20:1-OH/C18:1-DC 0.110.24 0.62 0.01 0.10 0.12 0.01 0.15 0.08 −0.06 −0.17 −0.12 C20-OH/C18-DC0.15 0.15 0.62 0.00 −0.01 −0.01 0.14 0.07 0.03 0.00 0.05 −0.14 C22 0.040.03 0.00 0.06 0.01 0.05 −0.07 −0.03 0.00 0.03 −0.46 0.41C8:1-OH/C6:1-DC 0.46 0.02 0.12 −0.09 −0.11 0.31 0.02 0.46 −0.03 0.13−0.06 0.12 C8:1-DC 0.56 0.08 0.10 −0.03 −0.07 0.30 0.10 0.44 −0.06 0.010.18 −0.09 C16:2 0.17 0.47 0.34 0.09 0.30 0.05 −0.24 0.03 0.22 −0.11−0.26 0.03 C16:1 0.39 0.62 0.27 0.00 0.32 −0.03 −0.14 −0.09 0.06 −0.17−0.02 −0.07 C16:1-OH/C14:1-DC 0.22 0.36 0.32 −0.02 0.19 0.02 0.02 −0.060.18 −0.08 0.23 −0.08 C18:2-OH −0.02 0.24 0.29 −0.06 −0.06 0.00 0.09−0.03 0.01 0.17 −0.51 −0.08 C20:4 −0.24 0.58 0.15 0.07 −0.13 0.06 0.030.22 0.02 0.16 −0.25 −0.01 FFA 0.10 0.23 −0.13 0.05 0.19 −0.01 −0.44−0.36 −0.22 −0.33 0.02 0.22 HBUT 0.17 0.24 0.15 −0.05 0.85 0.02 −0.09−0.08 −0.03 −0.22 −0.04 0.05 KET 0.17 0.20 0.15 −0.04 0.87 0.01 −0.07−0.06 −0.03 −0.20 −0.01 0.04

Further adjustment for lipids showed continued association with CAD,although Factor 4 was not significant in the initial group (initialgroup: factor 1, P=0.0002; factor 4, P=0.59; factor 9, P=0.02;replication group: factor 1, P=0.01; factor 4, P=0.02; factor 9,P=0.004). Although we adjusted for diabetes, given studies showingrelationships between metabolites with insulin resistance, we furtheradjusted the base multivariable model for fasting glucose. Theseanalyses revealed a continued significant association with CAD (initialgroup: factor 1, P=0.02; factor 4, P=0.02; factor 9, P=0.003;replication group: factor 1, P=0.03; factor 4, P=0.05; factor 9,P=0.02).

Stratified analyses suggested stronger association between factors 4 and9 with CAD in non-diabetics as compared with diabetics (Table 11), withminimal or no discernable signal in diabetics, but no consistentdifferences in association with CAD by smoking (Table 12).

TABLE 11 Association of PCA Derived Metabolomic Factors with CAD,Stratified by Diabetes. P-values for the association of PCA-derivedmetabolomic factors with CAD, stratified by a medical history ofdiabetes, are presented. Unadjusted p-values and p-values adjusted forhypertension, smoking, dyslipidemia, family history and BMI (and alsofor age, race and sex in the Replication Group) are presented. InitialGroup Replication Group Diabetics Non-Diabetics Diabetics Non-DiabeticsFactor Name Unadj Adj Unadj Adj Unadj Adj Unadj Adj 1 Medium ChainAcylcarnitines 0.007 0.01 0.03 0.19 0.15 0.09 0.42 0.26 2 Long ChainAcylcarnitines 0.70 0.65 0.12 0.46 0.06 0.08 0.23 0.15 3 Long ChainDicarboxyl/Hydroxyl Acylcarnitines 0.85 0.77 0.04 0.18 0.53 0.54 <0.00010.002 4 BCAA Related 0.12 0.09 <0.0001 0.0003 0.86 0.75 0.005 0.0004 5Ketone Related 0.06 0.11 0.56 0.97 0.13 0.23 0.95 0.94 6 Various 0.010.07 0.17 0.10 0.33 0.88 0.25 0.69 7 Amino Acids 0.41 0.32 0.38 0.270.61 0.69 0.03 0.08 8 Dicarboxyis 0.18 0.11 0.76 0.82 0.80 0.88 0.060.28 9 Urea Cycle Related 0.69 0.98 0.0005 0.0002 0.53 0.44 0.02 0.00210 Short Chain Acylcarnitines 0.09 0.25 0.16 0.35 0.90 0.87 0.89 0.85 11Various 0.14 0.14 0.99 0.27 0.19 0.04 0.08 0.19 12 Various 0.11 0.100.61 0.52 0.06 0.11 <0.0001 <0.0001

TABLE 12 Association of PCA Derived Metabolomic Factors with CAD,Stratified by Smoking. P-values for the association of PCA-derivedmetabolomic factors with CAD, stratified by smoking (currently smokingor not), are presented. Unadjusted p-values and p- values adjusted fordiabetes, hypertension, dyslipidemia, family history and BMI (and alsofor age, race and sex for the Replication Group) are presented. InitialGroup Replication Group Smokers NonSmokers Smokers Non-Smokers FactorName Unadj Adj Unadj Adj Unadj Adj Unadj Adj 1 Medium ChainAcylcarnitines 0.03 0.09 0.009 0.05 0.38 0.18 0.14 0.16 2 Long ChainAcylcarnitines 0.15 0.05 0.87 0.64 0.24 0.15 0.04 0.07 3 Long ChainDicarhoxyl/Hydroxyl Acylcarnitines 0.08 0.33 0.77 0.81 0.48 0.50 <0.00010.0005 4 BCAA Related 0.003 0.07 0.24 0.23 0.27 0.12 0.02 0.005 5 KetoneRelated 0.14 0.84 0.76 0.46 0.24 0.71 0.83 0.70 6 Various 0.82 0.32 0.460.62 0.89 0.87 0.96 0.77 7 Amino Acids 0.16 0.18 0.69 0.41 0.02 0.100.84 0.79 8 Dicarhoxyls 0.39 0.12 0.42 0.79 0.66 0.90 0.05 0.16 9 UreaCycle Related 0.14 0.58 0.0001 0.0004 0.04 0.05 0.02 0.03 10 Short ChainAcylcarnitines 0.14 0.41 0.08 0.27 0.37 0.39 0.48 0.58 11 Various 0.830.94 0.40 0.08 0.23 0.11 0.07 0.15 12 Various 0.15 0.47 0.97 0.60 0.00040.002 0.0002 0.0001

Additional adjustment for ten classes of medications had minimalinfluence on the relationship between factors and CAD in the initialgroup (factor 1, adjusted P=0.009; factor 4, P=0.03; factor 9, P=0.003),but were no longer significant in the replication group (factor 1,P=0.02; factor 4, P=0.19; factor 9, P=0.14). We also performed similaranalyses restricted to those individuals with available medication data,in the combined datasets to optimize power (N=416). These results showedcontinued association between factors 4 and 9 with CAD, althoughattenuated (factor 4: unadjusted model, p=0.0009; model adjusted for CADrisk factors, P=0.03; model adjusted for CAD risk factors andmedications, P=0.05; factor 9: unadjusted model, P=0.0003; modeladjusted for CAD risk factors, P=0.002; model adjusted for CAD riskfactors and medications, P=0.007).

Results presented are unadjusted for multiple comparisons. We used PCAto account for co-linearity of metabolites. Of the individualmetabolites, only glutamate/glutamine would survive Bonferronicorrection. Factors 4 and 9 would survive Bonferroni correction at thelevel of factors (P<0.004).

Association of Metabolite Profiles with Prevalent Myocardial Infarction

To examine association of these metabolites with a more severephenotype, we evaluated the relationship of the PCA-derived factors incases with a prior history of MI compared with controls free of CAD(initial group N=86 MI cases, replication group N=61 MI case). The twofactors (4 and 9) that were associated with CAD were also associatedwith prior MI in both groups (Table 9).

Assessment of Model Fit and ROC Curves for CAD

To further quantify the independent association of metabolite factorswith CAD, logistic regression models were constructed: (1) clinicalmodel; (2) clinical model plus factors 4 and 9; and (3) clinical modelplus all metabolite factors. Factors 4 and 9 were independentlyassociated with CAD in both the initial group (factor 4: odds ratio [OR]1.42; 95% CI, 1.09 to 1.84, P=0.01; factor 9: OR 0.69, 95% CI, 0.53 to0.90, P=0.006) and the replication group (factor 4: OR1.42; 95% CI 1.06to 1.89, P=0.02; factor 9: OR 0.67; 95% CI 0.48 to 0.92, P=0.01).Measure of model fit and ROC curves (FIG. 1) in the initial group showedmodestly greater discriminative capability for models containing factors4 and 9 (c-statistic 0.778), with some improvement with addition of allfactors (c-statistic 0.804), above the model containing only clinicalvariables (c-statistic 0.756; P=0.06 for comparison of clinical model toclinical model plus factors 4 and 9; P=0.003 for comparison of clinicalmodel to clinical model plus all factors). In the replication group,there was a slightly higher c-statistic with the addition of factors 4and 9 to the clinical model (c-statistic 0.773) than for the clinicalmodel alone (c-statistic 0.743), but more dramatic improvement withaddition of all factors (c-statistic 0.874; P=0.04 for comparison ofclinical model to clinical model plus factors 4 and 9; and P<0.0001 forcomparison of clinical model to clinical model plus all factors).

Given it is standard of care to measure lipids in patients in whom adiagnosis of CAD is being considered, and that CRP is a recognizedbiomarker of cardiovascular disease, we reconstructed these modelsincluding lipids and CRP. These analyses revealed a higher clinicalmodel fit in both initial and replication groups (c-statistic 0.842 and0.778, respectively). The addition of factors 4 and 9 to the clinicalmodel inclusive of lipids and CRP resulted in no improvement in thediscriminative ability of the model in the initial group (c-statistic0.848, P=0.31 for comparison with clinical model), with some improvementwith addition of all factors (c-statistic 0.865, P=0.01 for comparisonwith clinical model). However, the magnitude of improvement in theclinical model with addition of metabolite factors remained similar andlarge in the replication group (c-statistics: clinical model inclusiveof lipids and CRP, 0.778; clinical model+factors 4 and 9, 0.799, P=0.08;clinical model+all metabolite factors, 0.900, P=0.0001 for comparisonwith clinical model).

Metabolite Factors and Risk of Subsequent Cardiovascular Events

During a median of 2.72 years of follow-up, 74 of 314 CAD cases had anincident cardiovascular event. In unadjusted comparisons, factor 8(short-chain dicarboxylacylcarnitines) was highly associated withoccurrence of death or MI (FIG. 2; highest versus lowest tertile hazardratio [HR] 2.50; 95% CI, 1.47 to 4.17; P=0.0008; highest versus middletertile HR 2.33; 95% CI, 1.39 to 3.85; P=0.002). The strength of thisassociation was somewhat attenuated after adjustment for CAD riskfactors, CADindex, age, race, sex, ejection fraction, creatinine andtreatment with CABG after catheterization (highest versus lowesttertile: HR1.67; 95% CI, 0.88 to 3.13; P=0.11; highest versus middletertile: HR1.89; 95% CI 1.09 to 3.33; P=0.03). Factor 1 was alsoassociated with the occurrence of death/MI in unadjusted comparisons(highest versus lowest tertile HR1.85; 95% CI, 1.06 to 3.23; P=0.03;highest versus middle tertile HR1.79; 95% CI, 1.02 to 3.03; P=0.04), butwas no longer significant after adjustment (P=0.14 and 0.05,respectively).

To validate these findings, we performed metabolomic profiling in anindependent case-control dataset (‘event-replication’ group). In thisgroup, factor 8 was associated with cardiovascular events (unadjustedOR1.52; 95% CI, 1.08 to 2.14; P=0.01; adjusted OR1.82; 95% CI, 1.08 to3.50; P=0.03), with higher scores in cases who suffered subsequentcardiovascular events versus event-free controls. Individual metaboliteswithin the factor were also significantly different (P<0.05) betweencases and controls, with a similar direction of effect as observed inthe original ‘event’ dataset.

This example demonstrates that peripheral blood metabolite profiles areindependently associated with the presence of CAD, and add to thediscriminative capability for CAD compared with models containing onlyclinical variables. Further, we report a specific metabolite clusterthat independently predicts subsequent cardiovascular events inindividuals with CAD.

Example 2 Heritability of CAD Materials and Methods

Study Population. The GENECARD study enrolled 920 families to performaffected-sibling-pair linkage for identification of genes forearly-onset CAD (before age 51 for men, age 56 for women) (Hauser etal., 2003, Am Heart J, 145, 602-613). Families with at least twosiblings each of whom met the criteria for early-onset CAD (before age51 for men, age 56 for women) were recruited. Unaffected family memberswere defined as no clinical evidence of CAD and age greater than 55years for men (greater than 60 years for women). From this cohort, weselected eight representative families we believed would be particularlyinformative, based on availability of a relatively large number offamily members and a heavy burden of CAD in the proband and surroundinggenerations (FIG. 3). These families were recontacted; theaffected-sibling-pair and family members not previously enrolled wereascertained regardless of CAD, focused on offspring of theaffected-sibling-pair. This ascertainment strategy was based on thehypothesis that if abnormalities in metabolic profiles precededdevelopment of CAD in these families, that significant concordance ofmetabolite levels within families would be evident even in the absenceof overt CAD in the offspring. Sample collections within a given familywere done at several different times and at different locations, by asingle experienced phlebotomist. Blood samples were promptly processedafter collection via peripheral venous phlebotomy (within minutes),frozen as soon as possible thereafter (at most within 12 hours with themajority of samples being frozen within 1-2 hours of collection), andstored as plasma samples in EDTA-treated tubes at −80° C. Samples werecollected as often as possible in a fasting state; however, theconsistency of this could not be determined. Institutional Review Boardsapproved study protocols; informed consent was obtained from eachsubject.

Biochemical measurements. Frozen plasma samples were used toquantitatively measure targeted metabolites, including 37 acylcarnitinespecies, 15 amino acids, nine free fatty acids and conventionalanalytes, ketones and C-reactive protein (CRP). Sample preparation andcoefficients of variation have been reported (Haqq et al., 2005 ContempClin Trials, 26, 616-625). The laboratory was blinded to familyidentifiers and case-control status. Assay ranges are 0.05-40 micromolar(μM) (acylcarnitines); 5-1000 μM (amino acids); and 1-1000 mmol/L (fattyacids). For simplicity, the clinical shorthand of metabolites is used(Table 1). Intra-individual variability was assessed in samples fromfive individuals for which repeat profiling was performed on the samesample on five separate days. Coefficients-of-variation and correlationconfirmed minimal inter-assay variability (Table 1).

Conventional metabolite analysis. Standard clinical chemistry methodswere used for conventional metabolites, including glucose, totalcholesterol, high-density-lipoprotein (HDL)- and low-density-lipoprotein(LDL) cholesterol, and triglycerides with reagents from RocheDiagnostics (Indianapolis, Ind.); and free fatty acids (total) andketones (total and 3-hydroxybutyrate) with reagents from Wako (Richmond,Va.). All measurements were performed using a Hitachi 911 clinicalchemistry analyzer.

Acylcarnitines and amino acids. Proteins were first removed byprecipitation with methanol. Aliquoted supernatants were dried, and thenesterified with hot, acidic methanol (acylcarnitines) or n-butanol(amino acids). Acylcarnitines and amino acids were analyzed by tandem MSwith a Quattro Micro instrument (Waters Corporation, Milford, Mass.).Thirty-seven acylcarnitine species and 15 amino acids in plasma wereassayed by our previously described methods (Millington et al., 1990, JInherit Metab Dis, 13, 321-324; An et al., 2004, Nat Med, 10, 268-274;Wu et al., 2004, J Clin Invest, 113, 434-440). Leucine/isoleucine(LEU/ILE) are reported as a single analyte because they are not resolvedby our MS/MS method, and include contributions from alto-isoleucine andhydroxyproline. Under normal circumstances these isobaric amino acidscontribute little to the signal attributed to LEU/ILE. In addition, theacidic conditions used to form butyl esters results in partialhydrolysis of glutamine to glutamic acid and of asparagine to aspartate.Accordingly, values that are reported as GLU/GLN or ASP/ASN are notmeant to signify the molar sum of glutamate and glutamine, or ofaspartate and asparagine, but rather measure the amount of glutamate oraspartate plus the contribution of the partial hydrolysis reactions ofglutamine and asparagine, respectively.

Free fatty acids. Free fatty acids were gently methylated usingiodomethane and purified by solid-phase extraction (Patterson et al.,1999, J Lipid Res, 40, 2118-2124). Derivatized fatty acids were analyzedby capillary gas chromatography/mass spectrometry (GC/MS) using a TraceDSQ instrument (Thermo Electron Corporation, Austin, Tex.). Due tosample volume considerations, only 80 of the 117 individuals (five outof eight families) had free fatty acid measurements performed.

All mass-spectrometric analyses employed stable-isotope-dilution.Quantification of the foregoing “targeted” intermediary metabolites wasfacilitated by addition of mixtures of known quantities ofstable-isotope internal standards to samples, from Isotec (St. Louis,Mo.), Cambridge Isotope Laboratories (Andover, Mass.) and CDN Isotopes(Pointe-Claire, Quebec, CN) (Table 3).

Heritability analysis. Heritabilities were calculated using theSequential Oligogenic Linkage Analysis Routines (SOLAR) software version4.0.7 (Almasy and Blangero, 1998, Am J Hum Genet, 62, 1198-1211), whichuses maximum-likelihood methods to estimate variance components,allowing incorporation of fixed effects for known covariates andvariance components for genetic effects. This approach appropriatelyaccounts for correlation between all family members and allowsincorporation of extended pedigrees such as is present in the currentstudy. The total variation is partitioned into components for additivegenetic variance and environmental variance, as well as a residual(unexplained) variability. The program uses the pedigree covariancematrix

Ω=2Φ^(σ) ^(g) ² +I ^(σ) ^(e) ²

where Ω is the covariance matrix, Φ is the matrix of kinship values,i^(σ) ^(g) ² is the additive genetic variance, I represents the identitymatrix, and σ_(e) ² is the random environmental variance (Almasy et al.,1998, supra). This model allows for complex pedigree data (i.e. beyondparent-offspring pairs) and hence, the resulting heritability estimatesare more accurate than those obtained using only nuclear family members.For the current study, all sampled individuals from the pedigree wereentered into the variance components models, including unaffectedoffspring, cousins, and married-in family members. Incorporation ofmarried-in family members (i.e. genetically unrelated but with sharedenvironment) allows for better estimation of the environmental componentof intrafamilial clustering of traits.

Values considered outliers were excluded from heritability analyses,defined as values falling outside of the mean±4SD (one-two outliers foreach of 24 of the metabolites). Metabolite measurements below the lowerlimits of quantification (LOQ) were given a value of LOQ/2. Fourmetabolites having >25% of samples below LOQ were not further analyzed(C6, C5-OH:C3-DC, C4DC, and C10:2 acylcarnitines). All measurements werenatural log-transformed prior to analysis, resulting in most metabolitesapproximating a normal distribution, an important consideration forvariance components analysis. Eighteen metabolites did not meet thiscriterion, and therefore, linear regression models adjusted forbody-mass index (BMI), age, sex, CAD, diabetes mellitus (DM (yes/no),hypertension (yes/no), and dyslipidemia (yes/no) were constructed foreach of these metabolites, and the residuals were used for heritabilityestimates. Given occasional low trait standard deviations formetabolites (<0.5), all log transformed metabolites were multiplied by afactor of 4.7 prior to analysis.

Polygenic heritability models were then constructed. For the normallydistributed metabolites (the majority of metabolites), polygenicheritability models were calculated using the log-transformed values,adjusting for age, sex, BMI, DM, dyslipidemia, hypertension and CAD. Theproband and family members were not selected based on any metabolitevalues; however, the potential for ascertainment bias exists. Therefore,analyses were corrected based on which of the family members (proband)was the index member for ascertainment of the family for early-onsetCAD. To account for factors such as diet (which are shared in householdsbut are presumably not genetic) an additional variance componentparameter corresponding to the fraction of variance associated with theeffect of a common household (included in the model by a marker forresidential address), was added to each model. All residual kurtoses forthe final polygenic model were within normal range (i.e. <0.8), exceptfor two amino acids (serine and phenylalanine), eleven acylcarnitines(C5, C10, C10:1, C10:3, C12:1, C14, C14-OH:C12-DC, C16-OH:C14-DC,C18:1-OH, C18:1-DC, and C18-DC:C20-OH) and three free fatty acids(FAC14:0, FAC16:1, FAC18:1). For these metabolites, removal of 1-4 ofthe most extreme values was necessary, which then resulted in a normalresidual kurtosis. Two acylcarnitines required removal of a largernumber of outliers to achieve a normal residual kurtosis (C16-OH:C14-DCand C12-OH:C10-DC), and hence, these results should be interpretedaccordingly. For the eighteen non-normally distributed metabolites,standardized residuals from adjusted regression models were used toestimate heritabilities using SOLAR, but since the normalized deviateswere already adjusted for relevant covariates heritability models usingthese residuals were not further adjusted. Estimates of the proportionof variance explained by clinical covariates are reported for thesenon-normally distributed metabolites as estimated using the adjustedpolygenic model constructed from the log-transformed crude values.

For understanding quantitative differences in metabolites betweenfamilies, multivariable generalized linear models adjusted for sex, age,BMI, CAD, DM, dyslipidemia and hypertension, were used to compare meanmetabolite levels between families.

Unsupervised principal components analyses. Given that many metabolitesreside in overlapping pathways, correlation of metabolites is expected.To understand the correlation, we used principal components analysis(PCA) to reduce the large number of correlated variables into clustersof fewer uncorrelated factors using raw metabolite values withoutremoval of outliers. The factor with the highest “eigenvalue” accountsfor the largest amount of the variability within the dataset.Standardized residuals calculated for each metabolite from linearregression models adjusted for age, sex, BMI, DM, and CAD, were used asinput for PCA. PCA using residuals is recommended when, as in this case,the units for each variable vary significantly in magnitude (Johnson andWichern D. W., 1988, Applied Multivariate Statistical Analysis. PrenticeHall, Englewood Cliffs, N.J.). Factors with an eigenvalue ≧1.0 wereidentified based on the commonly employed Kaiser criterion (Kaiser,1960, Educational and Psychological Measurement, 20, 141-151). Varimaxrotation was then performed to produce interpretable factors.Metabolites with a factor load ≧|0.4| are reported as composing a givenfactor, as is commonly used as an arbitrary threshold (Lawlor et al.,2004, Am J Epidemiol, 159, 1013-1018). Scoring coefficients were thenused to compute factor scores for each individual (consisting of aweighted sum of the values of the standardized metabolites within thatfactor, weighted on the factor loading calculated for each individualmetabolite). These factor scores were then used to calculateheritabilities for each factor with SOLAR as detailed above, using apolygenic model not further adjusted for covariables. Removal of 1-4 ofthe most extreme values for several of the factors was necessary toachieve a normal residual kurtosis.

As all analyses were exploratory in nature and given collinearity of themetabolites, nominal two-sided p-values unadjusted for multiplecomparisons are presented, however results interpreted in the context ofa conservative Bonferroni correction are reported. Nominal statisticalsignificance was defined as p-value<0.05. Statistical analyses used SASversion 9.1 (SAS Institute, Cary N.C.), other than for heritabilityestimates which used SOLAR (Almasy et al., 1998, supra).

RESULTS AND DISCUSSION

Heritability Analysis. Metabolic profiling was performed on 117individuals within eight multiplex Caucasian families (FIG. 3) from theGENECARD study of premature CAD. Of note, the majority of family memberssampled for this study were as-yet-unaffected offspring of the originalaffected-sibling-pair, but who, as members of these families, were athigh risk for development of premature CAD. As expected, there was ahigh burden of CAD risk factors, although the prevalence differedbetween families (Table 13).

TABLE 13 Clinical characteristics of GENECARD families. The overallbaseline clinical characteristics of the GENECARD cohort are presented,as well as baseline characteristics within each family. Overall Family 1Family 2 Family 3 Family 4 Family 5 Family 6 Family 7 Family 8 Variable(N = 117) (N = 22) (N = 3) (N = 22) (N = 9) (N = 18) (N = 27) (N = 9) (N= 7) Age (SD) 45.62 49.77 39.00 39.27 49.22 49.33 44.04 46.33 49.29(15.82) (16.12) (18.25) (15.85) (11.12) (17.82) (15.38) (15.00) (15.96)Sex (% female) 48.7% 36.4% 66.7% 59.1% 66.7% 44.4% 48.2% 44.4% 42.9%Diabetes (%) 9.4% 13.6% 0.0% 9.1% 22.2% 16.7 0.0% 11.1% 0.0%Hypertension (%) 36.8% 36.4% 66.7% 36.4% 33.3% 44.4% 22.2 33.3% 71.4%Dyslipidemia (%) 35.0% 45.5% 0.0% 31.8% 44.4% 44.4% 22.2 44.4% 28.6% BMI(SD) 28.68 28.77 32.02 26.73 35.59 30.19 27.43 27.98 25.81 (5.70) (5.86)(7.13) (5.90) (7.18) (5.38) (4.05) (3.30) (4.00) Total cholesterol191.12 180.68 192.33 187.86 211.78 197.22 181.56 234.67 171.86 mean (SD)(43.20) (32.64) (23.18) (60.56) (43.42) (46.90) (34.30) (25.23) (8.32)(mg/dL) HDL cholesterol 45.34 39.70 51.67 56.10 48.20 45.54 40.56 39.9747.66 mean (SD) (15.18) (13.13) (3.70) (20.69) (15.18) (13.35) (11.46)(10.77) (9.32) (mg/dL) LDL cholesterol 117.67 102.48 138.30 116.10137.46 124.76 116.80 140.11 92.16 mean (SD) (35.94) (22.55) (30.58)(46.91) (35.12) (41.79) (26.99) (38.36) (17.15) (mg/dL) Triglycerides161.63 176.91 91.67 90.23 120.78 141.94 188.26 270.11 229.00 mean (SD)(115.02) (99.71) (19.66) (67.75) (54.97) (81.08) (125.14) (170.20)(154.39) (mg/dL) C-reactive protein 3.42 2.49 2.47 2.36 4.92 5.27 2.991.68 7.66 mean (SD) (3.61) (1.60) (1.69) (2.16) (5.37) (4.88) (2.29)(2.33) (6.70) (mg/L) HDL: high-density lipoprotein; LDL: low-densitylipoprotein; BMI: body-mass-index

We found high heritabilities for conventional risk factors such aslipids and BMI (FIG. 4). Total ketones (h² 0.75, p=3.8×10⁻⁸) had thehighest heritability among the metabolites analyzed by non-massspectrometry-based methods, with similarly high heritability of theindividual ketone β-hydroxybutyrate (h² 0.51, p=0.004). Among analytesmeasured by mass spectrometry, several amino acids had high heritability(FIG. 5, Table 14). Arginine (ARG) had the highest score (h² 0.80,p=1.9×10⁻¹⁶), with strong heritabilities also for glutamine/glutamate(GLX; h² 0.73, p=0.00006), alanine (ALA; h² 0.55, p=0.00002), proline(PRO; h² 0.52, p=0.00004), ornithine (ORN, h² 0.48, p=0.000005),phenylalanine (PHE; h² 0.46, p=0.0001), and the branched-chain aminoacids leucine/isoleucine (LEU/ILE; h² 0.39, p=0.00005) and valine (VAL;h² 0.44, p=0.00006). Of the free fatty acids (FIG. 5), FA-C20:4(arachidonic acid, a key component in inflammatory pathways) was themost heritable (h² 0.59, p=0.00005), as well as FA-C18:2 (linoleic acid,precursor to arachidonic acid, h² 0.48, p=0.002). Many acylcarnitinesalso had high heritabilities (FIG. 6, Table 14), the highest being theC18 acylcarnitines (C18, C18:1, and C18:2, h² 0.39-0.82,p=0.0000007-0.004); C14:1 (h² 0.79, p=0.0000002); C5:1 (h² 0.67,p=0.000003); the C10s (C10-OH:C8-DC, C10 and C10:1, h² 0.35-0.57,p=0.00003-0.02); C16 (h² 0.57, (p=0.0002); C4:Ci4 (h² 0.56, p=0.00003);short chain dicarboxylacylcarnitines (C5-DC, C6-DC, h² 0.45-0.51,p=0.003-0.004); and C2 acylcarnitine (h² 0.50, p=0.00008).Interestingly, estimates for the genetic component of the variability ofeach metabolite often exceeded the proportion of variance explained byclinical covariates (Table 14).

TABLE 14 Heritabilities, clinical covariates and household effects forindividual metabolites. Results for individual metabolites arepresented, including: heritability point estimates, standard error forthe heritability estimate, clinical covariates found to be significantin the polygenic model, the proportion of variance explained byhousehold effects, the p-value for the household effects, the proportionof variance in the metabolite explained by those clinical covariates,and the p-value for the heritabilities. Proportion House- Proportion Varhold Variance Heritability Short Name Heritability SE Covariates*Household p-value Covariates† p-value** C2 0.50 0.17 Age 0.06 0.3 0.180.00008 C3 0.35 0.13 HTN, Sex 0.08 0.06 0.18 0.0003 C4:Ci4 0.56 0.17CAD, Age, 0.01 0.4 0.29 0.00003 HTN, Dys, Sex C5:1 0.67 0.14 None 0.020.4 N/A 0.000003 C5 0.34 0.16 Sex 0.00 N/A 0.22 0.003 C4-OH 0.37 0.16Age 0.04 0.2 0.05 0.001 C8:1 0.27 0.18 BMI, Age 0.00 N/A 0.20 0.03 C80.45 0.23 Age 0.09 0.2 0.10 0.01 C5-DC 0.45 0.18 Age 0.00 N/A 0.05 0.003C6-DC 0.51 0.20 HTN, Age, 0.08 0.2 0.20 0.004 Sex C10:3 0.16 0.13 Age0.00 N/A 0.14 0.08 C10:1 0.57 0.16 Age, Sex 0.04 0.3 0.12 0.00003 C100.35 0.22 None 0.20 0.09 N/A 0.02 C10OH:C8DC 0.43 0.19 Age, Sex 0.00 N/A0.09 0.004 C12:1 0.44 0.22 DM 0.00 0.5 0.003 0.005 C12 0.34 0.22 Sex0.17 0.01 0.04 0.02 C12OH:C10DC 0.23 0.16 Age, Dys, Sex 0.00 N/A 0.110.04 C14:2 0.40 0.17 None 0.00 N/A N/A 0.003 C14:1 0.79 0.15 DM, Age0.00 N/A 0.03 <0.0001 C14 0.25 0.19 Dys 0.18 0.05 0.04 0.06 C14:1-OH0.23 0.19 None 0.01 0.4 N/A 0.08 C14-OH:12- 0.48 0.25 BMI 0.02 0.4 0.0030.03 DC C16 0.57 0.20 BMI, Age 0.00 N/A 0.15 0.0003 C16- 0.06 0.18 None0.04 0.3 N/A 0.36 OH:C14-DC C18:2 0.39 0.15 BMI 0.23 0.0007 0.11 0.0004C18:1 0.82 0.17 BMI, Age 0.02 0.4 0.06 0.0000007 C18 0.55 0.15 BMI, Age,0.03 0.3 0.09 0.00006 Sex C18:1-OH 0.00 — None 0.00 N/A N/A 0.50 C18-0.00 — None 0.007 0.5 N/A 0.50 OH:C16-DC C20 0.05 0.12 None 0.00 N/A N/A0.33 C18:1-DC 0.15 0.23 Age 0.00 N/A 0.04 0.23 C18- 0.40 0.25 None 0.00N/A N/A 0.04 DC:C20-OH C22 0.00 — None 0.08 0.2 N/A 0.50 GLY 0.33 0.14Sex, Dys 0.09 0.09 0.15 0.005 ALA 0.55 0.16 BMI 0.00 N/A 0.09 0.00002SER 0.25 0.17 BMI 0.19 0.02 0.13 0.06 PRO 0.52 0.16 Age, Sex 0.03 0.40.10 0.00004 VAL 0.44 0.14 Age, Sex, 0.04 0.2 0.18 0.00006 BMI LEU/ILE0.39 0.13 Sex 0.13 0.1 0.15 0.00005 MET 0.35 0.17 Sex 0.02 0.4 0.110.008 HIS 0.35 0.18 HTN 0.02 0.4 0.04 0.03 PHE 0.46 0.16 Sex, BMI, 0.00N/A 0.20 0.0001 HTN TYR 0.38 0.20 Sex, BMI 0.08 0.1 0.13 0.02 ASX 0.150.14 None 0.32 0.01 N/A 0.09 GLX 0.73 0.21 BMI, HTN, 0.00 N/A 0.230.00006 Sex ORN 0.48 0.13 Age, BMI, 0.04 0.3 0.16 0.000005 Dys CIT 0.390.18 CAD, Age 0.00 N/A 0.26 0.01 ARG 0.80 0.11 DM 0.13 0.004 0.003 1.9 ×10⁻¹⁶ FA-C14:0 0.51 0.24 DM 0.00 N/A 0.03 0.01 FA-C16:1 0.42 0.20 DM,Age 0.00 N/A 0.11 0.01 FA-C16:0 0.45 0.17 None 0.05 0.3 N/A 0.0008FA-C18:3 0.41 0.27 DM, CAD, 0.00 N/A 0.08 0.06 Dys FA-C18:2 0.48 0.18None 0.00 N/A N/A 0.002 FA-C18:1 0.40 0.19 Age, DM 0.00 N/A 0.13 0.01FA-C18:0 0.39 0.20 CAD, Age 0.08 0.3 0.06 0.01 FA-C20:4 0.59 0.19 None0.00 N/A N/A 0.00005 FA-C22:6 0.16 0.16 None 0.00 N/A N/A 0.15 FFA 0.450.16 CAD, Age, 0.14 0.11 0.09 0.0003 DM GLU 0.47 0.18 DM, Dys, Sex 0.020.4 0.23 0.001 TC 0.51 0.16 Age, CAD, 0.09 0.08 0.14 0.00007 DM, Dys HDL0.35 0.17 BMI, Sex 0.00 N/A 0.19 0.004 LDL 0.37 0.16 DM, BMI 0.11 0.020.08 0.004 TG 0.49 0.14 BMI 0.00 N/A 0.05 0.00001 Ket 0.75 0.13 None0.00 0.5 N/A 3.8 × 10⁻⁸  HBut 0.51 0.21 None 0.04 0.3 N/A 0.004 CRP 0.160.17 BMI 0.09 0.2 0.20 0.13 BMI 0.51 0.16 Age, HTN 0.06 0.2 0.14 0.0004*Clinical covariates (age, sex, BMI, hypertension, diabetes,dyslipidemia, CAD status) significant in polygenic model. †Proportion ofvariance in metabolite levels accounted for by clinical covariatessignificant in the model. **P-value for heritability estimate. DM:diabetes mellitus; HTN: hypertension; BMI: body-mass-index; CAD:affected with premature CAD; DYS: dyslipidemia.

Metabolomic Profiles within Families. Given these strong findings, wesought to understand quantitative differences in metabolites betweenfamilies. Multivariable linear models were used to test for differencesin metabolites between families. Of the amino acids, glutamate,ornithine, arginine, proline, histidine, phenylalanine, alanine andmethionine (all p<0.0001), leucine/isoleucine (p<0.0001) and valine(p=0.003) best differentiated families. Of the acylcarnitines, the C18(C18, C18:1, and C18:2) and the C14 acylcarnitines (C14, C14:1) (allp<0.0001), along with C5:1 (p<0.0001), and C2 (p<0.0001) acylcarnitinesbest differentiated families. Many free fatty acids differentiatedfamilies, the strongest being arachidonic and palmitic acid (bothp<0.0001). Of the conventional metabolites, ketones (p<0.0001) andβ-hydroxybutyrate (p=0.0001) best differentiated families.

Principal Components Analysis. Given correlation of metabolites inbiological pathways, we performed PCA to understand which clusters ofmetabolites were correlated and to identify factors that were mostheritable. Fifteen factors were identified, demonstrating biologicallyconsistent relationships (Table 15). Factors accounting for the largestamount of variance within the dataset were Factor 1 (short- andmedium-chain acylcarnitines); Factor 2 (long-chain free fatty acids);Factor 3 (long-chain acylcarnitines and amino acids [arginine,glutamate/glutamine, and ornithine] possibly reporting on mitochondrialfunction); Factor 4 (ketones, β-hydroxybutyrate, C2 and C4-OH[β-hydroxybutryl] acylcarnitines; all markers of terminal steps of fattyacid oxidation); and Factor 5 (amino acids, including branched-chainamino acids, and C3 and C5 acylcarnitines [by-products of branched-chainamino acid catabolism]). As expected, given results for individualmetabolites, many factors were heritable.

TABLE 15 Principal components analysis in GENECARD. Results of PCA inthe dataset are presented, including the key metabolites within eachfactor (i.e. those with a factor load ≧|0.4|); an overall biochemicaldescription of the key metabolites within each factor; and theeigenvalue, total and cumulative variance, heritability and p-value forthe heritability point estimate for each factor. Overall MetabolitesDescription of Eigen- Total Cum Factor within Factor* Factor value VarVar Heritability (SD) p-value 1 C2, C6-DC, C8, Short- and 11.88 0.200.20 0.39 (0.16) 0.0006 C8:1, C10, C10:1, medium-chain C10:3, C10-acylcarnitines OH:C8-DC, C12, C12:1, C14, C14:1, C14:2, C14:1-OH, C14-OH:C12-DC 2 Total FFA, FA- Free fatty acids 7.55 0.13 0.32 0.35 (0.20)0.02 C14:0, FA-C16:0, FA-C16:1, FA- C18:0, FA-C18:1, FA-C18:2, FA- C18:33 ARG, GLX, Amino acids, 5.89 0.10 0.42 0.40 (0.18) 0.002 ORN, C16, C18,long-chain C18:1, C18:2 acylcarnitines (markers of overall mitochondrialfunction) 4 C2, C4-OH, FFA oxidation 3.51 0.06 0.48 0.61 (0.17) 0.00004C14:1, C14:2, byproducts C14:1-OH, Ket, Hbut 5 ALA, LEU/ILE, Metabolites2.98 0.05 0.53 0.27 (0.15) 0.01 MET, PRO, TYR, involved in VAL, PHE, C5,amino acid C3, C20 catabolism 6 CIT, C5-DC, C8:1, Various 2.36 0.04 0.570.51 (0.17) 0.0008 C10:3 7 SER, GLY, CIT, Amino Acids 2.04 0.03 0.600.44 (0.28) 0.09 MET 8 C14-OH:C12-DC, Various 1.89 0.03 0.64 0.40 (0.18)0.003 C18:1-OH, C22 9 C12-OH:C10-DC, Various 1.86 0.03 0.67 0.51 (0.17)0.0003 C14, C14:1-OH, C20 10 C3, C4:Ci4, C22 Various 1.67 0.03 0.69 0.46(0.19) 0.002 11 ASX, HIS Amino Acids 1.48 0.02 0.72 0.33 (0.17) 0.005 12FAC22:6, Long chain free 1.37 0.02 0.74 0.36 (0.17) 0.007 FAC20:4, C20fatty acids 13 C16-OH:C14-DC Various 1.24 0.02 0.76 0.46 (0.20) 0.002 14PRO, ALA, Various 1.18 0.02 0.78 0.54 (0.16) 0.0001 C18:1-DC 15C18-DC:C20-OH Various 1.06 0.02 0.80 0.45 (0.19) 0.006 *Factor load≧|0.4; FFA: free fatty acids; Tot Var: total variance; Cum Var:cumulative variance

A comprehensive set of analytical tools was applied to gain a betterunderstanding of the biochemical and physiologic underpinnings ofcardiovascular disease, and how metabolomic profiles may relate to theknown genetic component of CAD risk. Targeted, quantitative metabolicprofiling was performed in multiplex families burdened with prematureCAD, the majority representing offspring of the affected generation thathad not yet developed CAD, but in whom we hypothesized similar metabolicprofiles as their affected family members, if such profiles wereheritable. High heritabilities were found for many metabolites, manywith higher heritabilities than for conventional risk factors. Thesehigh heritabilities suggest a strong correlation between genotype andphenotype, implying a strong genetic component to clustering of thesemetabolic signatures in families burdened with CAD.

In addition, several individual metabolites distinguished families, themost prominent being, among the amino acids, arginine, ornithine, andglutamate/glutamine; and among the lipid-derived metabolites, thelong-chain acylcarnitines C18:0, C18:1, and C18:2. These findingssuggest fundamental differences in mitochondrial function in thesefamilies, consistent with prior studies showing relationships betweenimpaired mitochondrial function and insulin resistance.

Given our studies were hypothesis-generating, we did not adjust formultiple comparisons. However, with a Bonferroni correction at the levelof the factors, nine factors remain significant (p<0.003). We did notaccount for dietary pattern (known influence on metabolites), renalfunction, or medications (unknown influence). To help minimize these“non-genetic” effects, we incorporated a household effect and includedmarried-in individuals, partially controlling for shared nutritional andother environmental effects. The measures of household effects suggestminimal influence on heritability estimates with high heritabilitiesdespite adjustment. Therefore, we believe our results reflect bothunderlying genetic and environmental effects, similar to traditionalcholesterol parameters. Accordingly, we found a significant householdeffect for LDL cholesterol (proportion of variance due to household0.11, p=0.02), but with a significant heritability despite adjustmentfor this environmental effect (h² 0.37, p=0.004).

Similarly, results could reflect differences in essential versusnon-essential metabolites. However, we found similar heritabilities forthe essential (h²=0.40, p=0.0004) and non-essential (h²=0.63, p=0.00002)amino acids when analyzed as groups, and for the essential (h²=0.50,p=0.003) compared with the non-essential (h²=0.33, p=0.03) fatty acids.Although underpowered for such analyses, we also examined therelationship of age with heritabilities related to these groups. Age wasa significant covariate on heritability estimates for both essential(valine) and nonessential (proline, ornithine, citrulline) amino acids(Table 14). For the free fatty acids, age was a covariate only fornonessential fatty acids (palmitoleic, oleic and stearic acid). We alsoexamined correlations of metabolites with age and found that bothessential (tyrosine, linoleic acid) and non-essential (glutamine,ornithine, citrulline, oleic acid) metabolites were significantlycorrelated with age (data not shown). Therefore, there does not seem tobe a consistent variation of metabolites with age, nor with heritabilityestimates, based on essential/non-essential groups. This may indicatethat fundamental and genetically controlled metabolic processes (e.g.mitochondrial or microsomal catabolic pathways) are influencing thelevels of both essential and non-essential metabolites that utilizethese common elements of the metabolic machinery.

Other factors that could impact heritability estimates includevariability in sample collection or processing. We used a standardizedprotocol to limit this type of variability, intra-individual variationwas low in a set of repeated assays, and family members were collectedat different locations and times.

A major strength of the study is the use of a very accurate, targeted,quantitative approach to metabolomic profiling, allowing us to dissectbiological mechanisms underlying CAD pathophysiology. In addition tofurthering the understanding of CAD pathophysiology, these results mayhave significant implications for risk prediction.

Each of the references cited herein is hereby incorporated by referencein its entirety.

1. A method for assessing risk of cardiovascular disease in a subjectcomprising: a) detecting the level of a least one metabolite in a samplefrom the subject, wherein the metabolite is selected from the groupconsisting of acylcarnitines, amino acids, ketones, free fatty acids andhydroxybutyrate; and b) comparing the level of the metabolite in thesample to a standard, wherein the level of the metabolite in the subjectis indicative of the risk of cardiovascular disease in the subject.
 2. Amethod for assessing the risk of cardiovascular disease in a subject,comprising: obtaining a sample from the subject; providing the sample toa laboratory for detection of metabolite levels in the sample, whereinthe metabolite is selected from acylcarnitines, amino acids, ketones,fatty acids and hydroxybutyrate; and receiving from the laboratory areport indicating metabolite levels in the sample, wherein the level ofthe metabolite is indicative of the risk of cardiovascular disease inthe subject.
 3. The method of claim 1, wherein the cardiovasculardisease is a cardiovascular event and the level of the metabolite in thesubject is indicative of the risk of a cardiovascular event in thesubject.
 4. The method of claim 3, wherein the metabolite detected instep (a) is a short-chain dicarboxylacylcarnitine metabolite.
 5. Themethod of claim 3, wherein the metabolite detected in step (a) isselected from the group consisting of Gly, Ala, Ser, Pro, Met, His, Phe,Tyr, Asx, Glx, Ornithine, Citrulline, arg, C2, C3, C4:C14; C5:1, C5,C4:OH, C14-DC:C4DC, C5-DC, C6-DC, C10:3, C10, C10-53 OH:C8DC, C12:1,C12, C12-OH:C10DC, C14:1-OH, C14-OH:C12-DC, C16, C16-OH/C14-DC, C18:2,C18-OH/C16-DC, C20, C20:1-OH/C18:1-DC, C20-OH/C18-DC, C8:1-OH/C6:1-DC,C8:1-DC, C16:1, C16:1-OH/C14:1-DC, C20:4, FFA, HBUT, and Ket.
 6. Themethod of claim 3, wherein the metabolite detected in step (a) includesthe metabolites of factor
 8. 7. The method of claim 3, wherein themetabolites detected in step (a) comprise citrulline, C5-DC, C6-DC,C8:1-OH/C6:1-DC, and C8:1-DC.
 8. The method of claim 3, wherein themetabolites detected in step (a) comprise ornithine, citrulline, C5,C14-DC:C4DC, C5-DC, C6-DC, C10-OH:C8DC, C8:1-OH/C6:1-DC, C8:1-DC, C20:4and FFA.
 9. The method of claim 1, wherein the cardiovascular disease iscoronary artery disease and the level of the metabolite in the subjectis indicative of the presence of coronary artery disease in the subject.10. The method of claim 9, wherein the metabolite detected in step (a)is a medium-chain acylcarnitine, a branched chain amino acid orassociated metabolite, or a metabolite associated with the urea cycle.11. The method of claim 9, wherein the metabolite detected in step (a)is selected from the group consisting of Pro, Leu/Ile, Met, Val, Glx,Citrulline, C2, C3, C4:Ci4; C5, C8, C8:1-OH/C6:1-DC, C10:1, C14:2,C14:1-OH, C16:2, C16:1, C16:2, C16:1, C16:1-OH/C14:1-DC, C18-OH/C16-DC,HBUT, and Ket.
 12. The method of claim 9, wherein the metabolitedetected in step (a) includes the metabolites of at least one of factor1, factor 4 or factor
 9. 13. The method of claim 9, wherein themetabolites detected in step (a) comprise Leu/Ile, Glx, 014:1-OH andC16:1-OH/C14:1-DC.
 14. The method of claim 13, wherein a level ofLeu/Ile greater than 170 mM is indicative of coronary artery disease.15. The method of claim 13, wherein a level of GLx greater than 128 mMis indicative of coronary artery disease.
 16. The method of claim 13,wherein a level of 014:1-OH less than 0.013 uM is indicative of coronaryartery disease.
 17. The method of claim 13, wherein a level ofC16:1-OH/C14:1-DC less than 0.0089 uM is indicative of coronary arterydisease.
 18. The method of claim 13, wherein the metabolites detected instep (a) further comprise C2, C5 and C18-OH/C16-DC.
 19. The method ofclaim 1, wherein the cardiovascular disease is coronary artery diseaseand the level of the metabolite in the subject is indicative of the riskof developing coronary artery disease in the subject.
 20. The method ofclaim 19, wherein the metabolites measured include the short- andmedium-chain acylcarnitine metabolites, branched chain amino acids andurea cycle related metabolites.
 21. The method of claim 19, wherein themetabolites measured include ketones. arg, ornithine, citrulline, glx,ala, val, leu/ile, pro, C2, C14:1, C18:1, C5:1, C4-i4, C18, C10:1 andFFA.
 22. The method of claim 1, wherein the level of at least twometabolites is detected in step (a).
 23. The method of claim 1, whereinthe sample is blood.
 24. The method of claim 1, wherein the level of themetabolite is detected using mass spectroscopy.
 25. The method of claim1, wherein the level of the metabolite is detected using a colorimetricor fluorometric assay.
 26. A method of developing a treatment plan for asubject comprising using the comparison of step (b) of any of thepreceding claims to develop a treatment plan based on the risk ofcardiovascular disease in the subject.